oildesign.ipynb

{
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      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/EvenSol/NeqSim-Colab/blob/master/oildesign.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H9UfpoardJU0"
      },
      "source": [
        "# Initialize NeqSim Project"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "epV_NOa_uVwt",
        "outputId": "21c5736b-04dd-4cae-d7b2-71de03e764ed"
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting git+https://github.com/equinor/neqsimpython.git\n",
            "  Cloning https://github.com/equinor/neqsimpython.git to /tmp/pip-req-build-kghqd01g\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/equinor/neqsimpython.git /tmp/pip-req-build-kghqd01g\n",
            "  Resolved https://github.com/equinor/neqsimpython.git to commit 3387328f5ef4fc29ba6e58b7cd869a50d9609ae3\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting jpype1 (from neqsim==2.5.8)\n",
            "  Downloading JPype1-1.4.1-cp310-cp310-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (465 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m465.3/465.3 kB\u001b[0m \u001b[31m3.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from neqsim==2.5.8) (1.5.3)\n",
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            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->neqsim==2.5.8) (1.16.0)\n",
            "Building wheels for collected packages: neqsim\n",
            "  Building wheel for neqsim (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for neqsim: filename=neqsim-2.5.8-py3-none-any.whl size=28196076 sha256=433eb83b5ad72989ae05955af1e6449570f28d84c9880ac5c8a6bd04ed4baeaa\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-i3odegf6/wheels/68/ff/ac/70b246ceb8f74c0d2b14d9dd5b7c6f735610bc7e341ec8f5de\n",
            "Successfully built neqsim\n",
            "Installing collected packages: jpype1, neqsim\n",
            "Successfully installed jpype1-1.4.1 neqsim-2.5.8\n"
          ]
        }
      ],
      "source": [
        "#!pip install neqsim\n",
        "!pip install git+https://github.com/equinor/neqsimpython.git\n",
        "import neqsim\n",
        "from neqsim.thermo.thermoTools import *\n",
        "import matplotlib\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import math\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uI8GKVAygpeB"
      },
      "source": [
        "#1. Project Strategy\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c-8g3tpbrAev"
      },
      "source": [
        "\n",
        "###Business case:\n",
        "Description of project consepts\n",
        "\n",
        "###Value drivers:\n",
        "NPV:\n",
        "* High NPV\n",
        "* Low BE (eg. 30 $/bbl)\n",
        "\n",
        "SSU:\n",
        "* CO2 emission; Less than 5 kg CO2/boe produced\n",
        "\n",
        "Hydrocarbon recovery\n",
        "* Maximize hydrocarbon recovery (resource recovery)\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jQbXEdM3vM87"
      },
      "source": [
        "## Risk handling"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vYOu0GgWvUrS"
      },
      "source": [
        "How to handle risk"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RiUNzkVB6Prg"
      },
      "source": [
        "## NPV calculation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UyTRUNj66S9x"
      },
      "source": [
        "Calculating the Net Present Value (NPV) for an oil field development involves estimating the future cash flows generated by the project, discounting those cash flows back to present value using an appropriate discount rate, and then subtracting the initial investment cost. Here's a step-by-step guide on how to calculate NPV for an oil field development:\n",
        "\n",
        "Estimate Cash Flows:\n",
        "Estimate the future cash flows that the oil field development is expected to generate over its lifetime. These cash flows can include revenues from oil production, as well as operating expenses, taxes, and other relevant costs. It's important to take into account factors such as production levels, oil prices, operating costs, and any potential changes in regulations or market conditions.\n",
        "\n",
        "Determine Discount Rate:\n",
        "Choose an appropriate discount rate that reflects the time value of money and the risk associated with the project. The discount rate should account for the project's specific risks and uncertainties. The discount rate is often derived from the company's cost of capital or a suitable rate of return required by investors.\n",
        "\n",
        "Calculate Present Value of Cash Flows:\n",
        "For each year or period, calculate the present value of the estimated cash flows using the chosen discount rate. The formula for calculating the present value (PV) of a future cash flow (CF) is:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0B9OlcKQ6Zxg",
        "outputId": "ee07722c-2182-425d-c397-1471323fe157"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "PV  0.9523809523809523  MNOK\n"
          ]
        }
      ],
      "source": [
        "import math\n",
        "CF = 1e6\n",
        "r = 0.05\n",
        "t= 1.0\n",
        "PV = CF / math.pow((1 + r),t)\n",
        "print('PV ' , PV/1e6, ' MNOK')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K8xH90IW6pP5"
      },
      "source": [
        "Where:\n",
        "\n",
        "PV = Present value of the cash flow\n",
        "CF = Future cash flow\n",
        "r = Discount rate\n",
        "t = Number of years in the future\n",
        "You'll need to calculate the present value for each year's cash flow.\n",
        "\n",
        "Sum the Present Values:\n",
        "Sum up all the present values of the estimated cash flows to get the total present value of the cash flows over the entire life of the project.\n",
        "\n",
        "Subtract Initial Investment:\n",
        "Subtract the initial investment cost required for the oil field development from the total present value of cash flows calculated in the previous step.\n",
        "\n",
        "Interpret the Result:\n",
        "\n",
        "If the calculated NPV is positive, it indicates that the project is expected to generate more cash inflows than outflows and is potentially a viable investment.\n",
        "If the NPV is negative, it suggests that the project's expected cash inflows are less than the outflows, which might make the project less attractive or not financially viable.\n",
        "It's important to note that NPV calculations heavily depend on the accuracy of the input data, such as cash flow estimates and discount rates. Additionally, sensitivity analysis can be performed by testing different scenarios and assumptions to understand the project's sensitivity to changes in variables like oil prices, production volumes, and costs.\n",
        "\n",
        "It's recommended to consult with financial experts, economists, and professionals with expertise in oil and gas industry evaluations when performing NPV calculations for complex projects like oil field developments."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7HqG9qAfmiKu"
      },
      "source": [
        "## Investment Equivalent (IE)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zh3eVa-fl95s"
      },
      "source": [
        "\n",
        "Investment Equivalent (IE) is a method used in project analysis to compare the relative attractiveness of investments that require different amounts of capital. It is a calculation that determines the initial cash outlay required for an investment with equivalent cash flow to the proposed project. The investment equivalent analysis is used to evaluate the cost-effectiveness of different investment projects in a company's portfolio.\n",
        "\n",
        "The Investment Equivalent is calculated as follows:\n",
        "\n",
        "$IE = Investment A ×   ((1 + r)n  - 1) / (r × (1 + r)n)$\n",
        "\n",
        "Where:\n",
        "- Investment A is the initial investment required for one project\n",
        "- r is the discount rate or cost of capital\n",
        "- n is the number of years of the project's life\n",
        "\n",
        "The IE calculation helps to compare investments with different initial investment costs. By determining the investment equivalent, an investor can evaluate the cost-effectiveness of different investment projects and choose the project with the highest net present value (NPV) or highest returns per dollar invested.\n",
        "\n",
        "In summary, the investment equivalent is a useful tool for comparing the initial capital costs of different investment projects to determine which investment has the best financial return. It enables investors to make informed decisions on which projects to pursue in order to maximize their returns.\n",
        "\n",
        "Sure, here are the equations for calculating Investment Equivalent:\n",
        "\n",
        "IE = Investment A ×   ((1 + r)n  - 1) / (r × (1 + r)n)\n",
        "\n",
        "where:\n",
        "\n",
        "- IE = Investment Equivalent\n",
        "- Investment A = initial investment required for one project\n",
        "- r = discount rate or cost of capital\n",
        "- n = number of years of the project's life\n",
        "\n",
        "To illustrate the use of the Investment Equivalent equation, let's say we have two potential projects with different investment requirements and cash flows:\n",
        "\n",
        "Project A requires an initial investment of $100,000 and is expected to generate cash flows of $25,000 per year for 5 years at a discount rate (r) of 10%.\n",
        "\n",
        "Project B requires an initial investment of $150,000 and is expected to generate cash flows of $35,000 per year for 5 years at a discount rate of 10%.\n",
        "\n",
        "To compare these projects on an equal footing, we can calculate the Investment Equivalent for each project using the above equation:\n",
        "\n",
        "IE of Project A = $100,000 × ((1 + 0.1)^5 - 1) / (0.1 × (1 + 0.1)^5) = $281,031.05\n",
        "\n",
        "IE of Project B = $150,000 × ((1 + 0.1)^5 - 1) / (0.1 × (1 + 0.1)^5) = $421,546.58\n",
        "\n",
        "Based on the Investment Equivalent calculation, Project B has a higher IE value, indicating that it requires a greater amount of initial capital investment to generate the same cash flows as Project A. Investors can use this information to make informed decisions on which project to pursue by comparing the investment costs and projected returns of each project."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D7cL-Npd7vPk"
      },
      "source": [
        "## BE calculation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UF_M4O6u7ydr"
      },
      "source": [
        "The breakeven oil price for an oil project is the price at which the project's revenues match its total costs, resulting in zero profit or loss. Calculating the breakeven oil price involves determining the point at which the project becomes financially self-sustaining. Here's how you can calculate the breakeven oil price for an oil project:\n",
        "\n",
        "Estimate Fixed Costs:\n",
        "Identify the fixed costs associated with the oil project. Fixed costs are those that do not vary with the level of production or sales. These could include items like lease or rental expenses, insurance, administrative salaries, etc.\n",
        "\n",
        "Estimate Variable Costs:\n",
        "Determine the variable costs related to the oil project. Variable costs change with the level of production. These costs might include expenses like drilling and extraction costs, transportation, labor directly tied to production, etc.\n",
        "\n",
        "Calculate Total Costs:\n",
        "Add up the fixed costs and variable costs to get the total costs for the project at a given level of production. The formula would be:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TZ9UeFnS77ZD"
      },
      "outputs": [],
      "source": [
        "Fixed_Costs = 1e6\n",
        "Variable_Costs_per_Unit = 1\n",
        "Units_of_Production  = 1.0\n",
        "Total_Costs = Fixed_Costs + (Variable_Costs_per_Unit * Units_of_Production)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iewTATXk8L1n"
      },
      "source": [
        "Estimate Revenue per Unit:\n",
        "Determine the revenue generated per unit of oil produced. This could be based on the expected sales price of the oil.\n",
        "\n",
        "Set Up Breakeven Equation:\n",
        "The breakeven equation is where the total costs equal the total revenue. At breakeven, the equation would be:\n",
        "\n",
        "Substituting in the revenue per unit and the total costs formula from earlier:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "M4SeqJAf8MvM"
      },
      "outputs": [],
      "source": [
        "Total_Revenue = Total_Costs\n",
        "#Fixed_Costs + (Variable Costs per Unit * Units of Production) = Revenue per Unit * Units of Production\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AraHl-DC8gdc"
      },
      "source": [
        "Solve for Breakeven Units:\n",
        "Solve the equation for the number of units of production (oil) needed to reach the breakeven point:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dR0Zt9EL8hUU"
      },
      "outputs": [],
      "source": [
        "#Breakeven_Oil_Price = Total_Fixed_Costs / Breakeven_Units_of_Production\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nBEVip248imL"
      },
      "source": [
        "Calculate Breakeven Oil Price:\n",
        "Divide the total fixed costs by the breakeven units of production to determine the breakeven oil price. This price represents the price at which the project covers all its costs."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Q5ChSjhg8k3e"
      },
      "outputs": [],
      "source": [
        "#Breakevn_Oil Price = Total_Fixed_Costs / Breakeven_Units_of_Production"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kERD7eaB8mTc"
      },
      "source": [
        "Keep in mind that breakeven calculations are based on certain assumptions and estimates, which can change over time due to various factors such as fluctuations in oil prices, changes in production costs, and unforeseen operational challenges. Conducting sensitivity analysis by considering different cost and price scenarios can help assess the project's robustness to changes in these variables. It's also important to consult with industry experts and financial professionals when performing breakeven analyses for oil projects."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uBh1yTRi9JMN"
      },
      "source": [
        "## CO2 emission calculation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kgx-rdP29Mh0"
      },
      "source": [
        "......"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-1mx70sde86a"
      },
      "source": [
        "# 2. Economy Basis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CX7alcTJfBJb"
      },
      "source": [
        "Input to economy ealuations"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FSy4t_7RfE_y"
      },
      "outputs": [],
      "source": [
        "oilprice = 40.0 #US$/boe\n",
        "salesgasprice = 1.0 # US$/SM3\n",
        "LPGprice = 100 # US$/tonn\n",
        "C5price = 100 # US$/m3\n",
        "\n",
        "discountrate = 0.05\n",
        "streamdaysperyear = 360\n",
        "\n",
        "taxoil = 0.25\n",
        "taxgas = 0.25\n",
        "taxLPG = 0.25\n",
        "taxC5 = 0.25"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J00M6V9Zu80N"
      },
      "source": [
        "#  3. Design Basis\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yt7JFy5t16om"
      },
      "source": [
        "##Description"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qTI8bL9i3w7r"
      },
      "source": [
        "Writing a design basis for an oil field involves providing a comprehensive outline of the project's objectives, requirements, and specifications. While the specific details may vary depending on the specific project and its location, here are the key components you should consider when writing a design basis for an oil field:\n",
        "\n",
        "1. Introduction:\n",
        "   - Provide a brief overview of the project, including its purpose and scope.\n",
        "   - Identify the stakeholders involved, such as the project owner, operators, and regulatory authorities.\n",
        "\n",
        "2. Project Objectives:\n",
        "   - Clearly state the primary objectives of the oil field project, such as maximizing production rates, optimizing recovery, or minimizing environmental impact.\n",
        "   - Specify any secondary objectives, such as cost efficiency, safety, or sustainability goals.\n",
        "\n",
        "3. Field Characteristics:\n",
        "   - Describe the geological and reservoir characteristics of the oil field, including the type of reservoir (conventional, unconventional, offshore, etc.), formation properties, fluid composition, and estimated reserves.\n",
        "   - Provide information on the field size, shape, and potential production rates.\n",
        "\n",
        "4. Facilities and Infrastructure:\n",
        "   - Outline the necessary facilities and infrastructure required for oil field operations, including drilling rigs, production platforms, pipelines, storage tanks, and processing facilities.\n",
        "   - Specify the anticipated production capacity and any planned expansions or phased development.\n",
        "\n",
        "5. Health, Safety, and Environment (HSE):\n",
        "   - Identify the key HSE considerations and standards to be followed throughout the project lifecycle, including regulatory compliance, risk assessment, emergency response plans, and environmental impact mitigation measures.\n",
        "   - Address specific concerns related to worker safety, pollution prevention, and ecosystem protection.\n",
        "\n",
        "6. Well Design and Drilling:\n",
        "   - Discuss the well design requirements, including the number and types of wells, well spacing, completion techniques, and well integrity measures.\n",
        "   - Define the drilling methods, casing and cementing procedures, and any special considerations for drilling in challenging environments.\n",
        "\n",
        "7. Production and Operations:\n",
        "   - Specify the production techniques and technologies to be employed, such as primary, secondary, or enhanced oil recovery methods.\n",
        "   - Describe the production processes, flow rates, separation and treatment facilities, and any associated transportation requirements.\n",
        "\n",
        "8. Monitoring and Control:\n",
        "   - Outline the monitoring and control systems to be implemented for production optimization, reservoir management, equipment performance, and safety.\n",
        "   - Identify the instrumentation, automation, and data management systems to be utilized.\n",
        "\n",
        "9. Project Schedule and Milestones:\n",
        "   - Provide a high-level timeline for the project, including key milestones, major activities, and anticipated durations.\n",
        "   - Identify any dependencies, critical path items, and potential risks or uncertainties.\n",
        "\n",
        "10. Project Execution and Quality Assurance:\n",
        "    - Describe the project execution strategy, including contracting approaches, procurement plans, and quality assurance and control procedures.\n",
        "    - Address any specific regulations, industry standards, or certifications required for compliance.\n",
        "\n",
        "11. Project Constraints and Assumptions:\n",
        "    - Highlight any known constraints or limitations that may impact the design or execution of the project, such as environmental restrictions, budgetary considerations, or technical challenges.\n",
        "    - Document any assumptions made during the design process that may affect the project outcomes.\n",
        "\n",
        "12. References:\n",
        "    - Include a list of references, such as industry standards, technical guidelines, or relevant research papers, that were consulted during the design basis preparation.\n",
        "\n",
        "Remember, this is a general framework, and you may need to adapt it to the specific requirements of your oil field project. Additionally, it is recommended to involve relevant subject matter experts, such as geologists, reservoir engineers, and HSE specialists, to ensure the accuracy and completeness of the design basis."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_WUr-A7aexa-"
      },
      "source": [
        "## Reservoir data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9oKykqekeZBI",
        "outputId": "3403fdce-4827-49c6-a836-125f56bf65df"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "oil volume at res cond (estimated)  845.25  Mm3\n",
            "OOIP (estimated)  483.0  MSm3 oe\n",
            "recoverable oil volume (estimated) 241.5  MSm3 oe\n",
            "OOIP  508.7584  MMm3\n",
            "recoverable volume  254.3792  MSm3 oe\n"
          ]
        }
      ],
      "source": [
        "initialReservoirPressure = 907 #bara\n",
        "initialReservoirTemperature = 100 #C\n",
        "referenceDepth = 5946\n",
        "freeWaterDepth = 6268\n",
        "porosity = 10 #up to 15\n",
        "permeability = 100 # 1-1000 mD\n",
        "waterSaturation = 25 #%\n",
        "saturationPressure = 495 # bara\n",
        "BoatPi = 1.75\n",
        "oilViscosityatResCond = 0.5 # cP\n",
        "temperaturegradient = 0.0296 #C/m\n",
        "recoveryFactor = 0.5\n",
        "\n",
        "lengthreservoir= 7000\n",
        "widthreservoir = 5000\n",
        "heightreservoir = freeWaterDepth-referenceDepth\n",
        "areaReservoir = lengthreservoir*widthreservoir\n",
        "\n",
        "#original oil in place (OOIP)\n",
        "# see how to calculate:\n",
        "#https://wiki.aapg.org/Predicting_hydrocarbon_recovery\n",
        "OOIP = areaReservoir*heightreservoir * (porosity/100) * (1.0 - waterSaturation/100) / BoatPi\n",
        "oilvolumeatreservoirconditions = areaReservoir*heightreservoir * (porosity/100) * (1.0 - waterSaturation/100)\n",
        "print('oil volume at res cond (estimated) ', oilvolumeatreservoirconditions/1e6, ' Mm3')\n",
        "print('OOIP (estimated) ', OOIP/1e6, ' MSm3 oe')\n",
        "recoverableOil = OOIP*recoveryFactor\n",
        "print('recoverable oil volume (estimated)', recoverableOil/1e6, ' MSm3 oe')\n",
        "\n",
        "recoverableOilVolumeMMbbl = 1600\n",
        "recoverableOilVolume = recoverableOilVolumeMMbbl* 1e6 * 158.987/1000.0 # m3\n",
        "OOIP = recoverableOilVolume/recoveryFactor\n",
        "print('OOIP ', OOIP/1e6, ' MMm3')\n",
        "print('recoverable volume ', recoverableOilVolume/1e6, ' MSm3 oe')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "Iu6KIfwQbHWz"
      },
      "outputs": [],
      "source": [
        "SWHP = 780 #bara\n",
        "FHWT = 120 #C\n",
        "welldesignpres = 900\n",
        "\n",
        "flowlinemaxpressure = 350 #bara\n",
        "\n",
        "seabedtemperature = 3 #\n",
        "\n",
        "designTemperatureProcessAfterSeaWaterCooler = 36.0"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "26rBySZLZvv5"
      },
      "source": [
        "##Field info and links"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZkPI83oTnEgZ"
      },
      "source": [
        "https://www.pgs.com/data-library/map/nsa/south-atlantic-margin/santos--campos/santos-vision/?lat=-25.301665629013435&long=-44.02997554747069&zoom=11\n",
        "\n",
        "\n",
        "https://www.rfdbase.com/web/Account/Login?ReturnUrl=%2Fweb"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oXCQ6IYp3SwL"
      },
      "source": [
        "## geo data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "so1JYnZSJV-2"
      },
      "source": [
        "Use libraries:\n",
        "\n",
        "https://github.com/gee-community/geemap\n",
        "\n",
        "https://developers.google.com/earth-engine\n",
        "\n",
        "https://www.youtube.com/watch?v=wGjpjh9IQ5I\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mfjgOp4WbSOt"
      },
      "outputs": [],
      "source": [
        "%%script false --no-raise-error\n",
        "\n",
        "!pip install geemap"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iR6ZopX0pX46"
      },
      "outputs": [],
      "source": [
        "%%script false --no-raise-error\n",
        "\n",
        "import ee\n",
        "import geemap\n",
        "\n",
        "u_lon = -23.650888\n",
        "u_lat = -45.505189\n",
        "zoom=10\n",
        "\n",
        "Map = geemap.Map(center=(u_lon, u_lat), zoom=zoom)\n",
        "\n",
        "\n",
        "dem = ee.Image('USGS/SRTMGL1_003')\n",
        "landcover = ee.Image(\"ESA/GLOBCOVER_L4_200901_200912_V2_3\").select('landcover')\n",
        "landsat7 = ee.Image('LANDSAT/LE7_TOA_5YEAR/1999_2003')\n",
        "states = ee.FeatureCollection(\"TIGER/2018/States\")\n",
        "\n",
        "dem_vis = {\n",
        "'min': 0,\n",
        "'max': 4000,\n",
        "'palette': ['006633', 'E5FFCC', '662A00', 'D8D8D8', 'F5F5F5']}\n",
        "\n",
        "landsat_vis = {\n",
        "    'min': 20,\n",
        "    'max': 200,\n",
        "    'bands': ['B4', 'B3', 'B2']\n",
        "}\n",
        "\n",
        "Map.addLayer(dem, dem_vis, 'SRTM DEM', True, 0.5)\n",
        "#Map.addLayer(landcover, {}, 'Land cover')\n",
        "#Map.addLayer(landsat7, landsat_vis, 'Landsat 7')\n",
        "\n",
        "Map"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dR7cWHnk0HCx"
      },
      "outputs": [],
      "source": [
        "%%script false --no-raise-error\n",
        "\n",
        "naip_ts = geemap.naip_timeseries(start_year=2009, end_year=2023)\n",
        "layer_names = ['NAIP ' + str(year) for year in range(2009, 2023)]\n",
        "print(layer_names)\n",
        "naip_vis = {'bands': ['N', 'R', 'G']}\n",
        "\n",
        "Map = geemap.Map(center=(u_lon, u_lat), zoom=zoom)\n",
        "Map.add_basemap('HYBRID')\n",
        "Map.ts_inspector(\n",
        "    left_ts=naip_ts,\n",
        "    right_ts=naip_ts,\n",
        "    left_names=layer_names,\n",
        "    right_names=layer_names,\n",
        "    left_vis=naip_vis,\n",
        "    right_vis=naip_vis,\n",
        ")\n",
        "Map"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RxUfHpWOvQm_"
      },
      "outputs": [],
      "source": [
        "%%script false --no-raise-error\n",
        "\n",
        "import ee\n",
        "\n",
        "# Trigger the authentication flow.\n",
        "ee.Authenticate()\n",
        "\n",
        "# Initialize the library.\n",
        "ee.Initialize()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "joK6xskq0yX_"
      },
      "outputs": [],
      "source": [
        "%%script false --no-raise-error\n",
        "\n",
        "# Define the urban location of interest as a point near Caraguatatuba, Brazil.\n",
        "u_lon = -23.650888\n",
        "u_lat = -45.505189\n",
        "u_poi = ee.Geometry.Point(u_lon, u_lat)\n",
        "\n",
        "u_lat2 = -25.3\n",
        "u_lon2 = -44.03"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BugQ_Rkhzb5r"
      },
      "outputs": [],
      "source": [
        "%%script false --no-raise-error\n",
        "\n",
        "import geopy.distance\n",
        "\n",
        "coords_1 = (u_lon, u_lat)\n",
        "coords_2 = (u_lon2, u_lat2)\n",
        "\n",
        "print(geopy.distance.geodesic(coords_1, coords_2).km)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FLnct3UN1vyC"
      },
      "outputs": [],
      "source": [
        "%%script false --no-raise-error\n",
        "\n",
        "import folium\n",
        "\n",
        "# Define the center of our map.\n",
        "lat, lon =u_lon, u_lat\n",
        "\n",
        "my_map = folium.Map(location=[lat, lon], zoom_start=8)\n",
        "my_map.add_child(folium.LatLngPopup())\n",
        "folium.Marker(location=[u_lat2, u_lon2],popup='Field location',tooltip='Click here to see Popup').add_to(my_map)\n",
        "folium.Marker(location=[lat, lon],popup='New process plant location',tooltip='Click here to see Popup').add_to(my_map)\n",
        "\n",
        "\n",
        "\n",
        "my_map"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "G-ZgHILZ0oMM"
      },
      "outputs": [],
      "source": [
        "%%script false --no-raise-error\n",
        "\n",
        "# Import the MODIS land cover collection.\n",
        "lc = ee.ImageCollection('MODIS/006/MCD12Q1')\n",
        "\n",
        "# Import the MODIS land surface temperature collection.\n",
        "lst = ee.ImageCollection('MODIS/006/MOD11A1')\n",
        "\n",
        "# Import the USGS ground elevation image.\n",
        "elv = ee.Image('USGS/SRTMGL1_003')\n",
        "\n",
        "# Import wind speed\n",
        "windSpeed = ee.ImageCollection('NOAA/GFS0P25')\n",
        "\n",
        "#ocean data\n",
        "oceandt = ee.ImageCollection('SIRGAS 2000')\n",
        "\n",
        "#bathdata\n",
        "bathy_dataset = ee.ImageCollection('NOAA/NGDC/ETOPO1')\n",
        "\n",
        "#landslides\n",
        "landslid = ee.ImageCollection('COPERNICUS/S2_HARMONIZED')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "r4Lw5O4lOCGv"
      },
      "outputs": [],
      "source": [
        "#scale = 100  # scale in meters\n",
        "#elv_urban_point = elv.sample(u_poi, scale).first().get('elevation').getInfo()\n",
        "#print('Ground elevation at point:', elv_urban_point, 'm')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xB2bVmMu0tR_"
      },
      "outputs": [],
      "source": [
        "%%script false --no-raise-error\n",
        "\n",
        "# Initial date of interest (inclusive).\n",
        "i_date = '2017-01-01'\n",
        "\n",
        "# Final date of interest (exclusive).\n",
        "f_date = '2020-01-01'\n",
        "\n",
        "# Selection of appropriate bands and dates for LST.\n",
        "lst = lst.select('LST_Day_1km', 'QC_Day').filterDate(i_date, f_date)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2RP2bBdDg8Ai"
      },
      "outputs": [],
      "source": [
        "%%script false --no-raise-error\n",
        "landslid = landslid.filter(ee.Filter.date(i_date, f_date)).median()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LVLhN4iZDjKt"
      },
      "outputs": [],
      "source": [
        "%%script false --no-raise-error\n",
        "# Import a dataset focusing on oceans/surface water properties.\n",
        "# Surface water temperature for exampel:\n",
        "sst = ee.ImageCollection('NASA/OCEANDATA/MODIS-Aqua/L3SMI')\\\n",
        "    .select(['sst']).filterDate('2017-01-01', '2018-01-01')\n",
        "\n",
        "# Define the mask\n",
        "mask = sst.mean().gte(-10)\n",
        "\n",
        "#Apply the mask\n",
        "wind = windSpeed.select('LST_Day_1km', 'QC_Day').filterDate(i_date, f_date)\n",
        "wind_img_u=wind.select('u_component_of_wind_10m_above_ground').filterDate(i_date,f_date).first()\n",
        "wind_img_v=wind.select('v_component_of_wind_10m_above_ground').filterDate(i_date,f_date).first()\n",
        "wind_img_uv=wind_img_u.add(wind_img_v)\n",
        "wind_img_uv = wind_img_uv.updateMask(mask).select('LST_Day_1km')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "O7VNp_xu4GWR"
      },
      "outputs": [],
      "source": [
        "%%script false --no-raise-error\n",
        "from IPython.display import Image\n",
        "# Define a region of interest with a buffer zone of 1000 km around Caraguatatuba.\n",
        "roi = u_poi.buffer(1e6)\n",
        "# Make pixels with elevation below sea level transparent.\n",
        "elv_img = elv.updateMask(elv.gt(0))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ie4SbWC7235V"
      },
      "outputs": [],
      "source": [
        "%%script false --no-raise-error\n",
        "def add_ee_layer(self, ee_image_object, vis_params, name):\n",
        "    \"\"\"Adds a method for displaying Earth Engine image tiles to folium map.\"\"\"\n",
        "    map_id_dict = ee.Image(ee_image_object).getMapId(vis_params)\n",
        "    folium.raster_layers.TileLayer(\n",
        "        tiles=map_id_dict['tile_fetcher'].url_format,\n",
        "        attr='Map Data &copy; <a href=\"https://earthengine.google.com/\">Google Earth Engine</a>',\n",
        "        name=name,\n",
        "        overlay=True,\n",
        "        control=True\n",
        "    ).add_to(self)\n",
        "\n",
        "# Add Earth Engine drawing method to folium.\n",
        "folium.Map.add_ee_layer = add_ee_layer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "InE91tPT4byx"
      },
      "outputs": [],
      "source": [
        "%%script false --no-raise-error\n",
        "# Reduce the LST collection by mean.\n",
        "lst_img = lst.mean()\n",
        "\n",
        "# Adjust for scale factor.\n",
        "lst_img = lst_img.select('LST_Day_1km').multiply(0.02)\n",
        "\n",
        "# Convert Kelvin to Celsius.\n",
        "lst_img = lst_img.select('LST_Day_1km').add(-273.15)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Vn803JE551xa"
      },
      "outputs": [],
      "source": [
        "%%script false --no-raise-error\n",
        "# Set visualization parameters for ground elevation.\n",
        "elv_vis_params = {\n",
        "    'min': 0, 'max': 4000,\n",
        "    'palette': ['006633', 'E5FFCC', '662A00', 'D8D8D8', 'F5F5F5']}\n",
        "\n",
        "# Set visualization parameters for land surface temperature.\n",
        "lst_vis_params = {\n",
        "    'min': 0, 'max': 40,\n",
        "    'palette': ['white', 'blue', 'green', 'yellow', 'orange', 'red']}\n",
        "\n",
        "# Select a specific band and dates for land cover.\n",
        "lc_img = lc.select('LC_Type1').filterDate(i_date).first()\n",
        "\n",
        "# Set visualization parameters for land cover.\n",
        "lc_vis_params = {\n",
        "    'min': 1,'max': 17,\n",
        "    'palette': ['05450a','086a10', '54a708', '78d203', '009900', 'c6b044',\n",
        "                'dcd159', 'dade48', 'fbff13', 'b6ff05', '27ff87', 'c24f44',\n",
        "                'a5a5a5', 'ff6d4c', '69fff8', 'f9ffa4', '1c0dff']\n",
        "}\n",
        "\n",
        "# Set visualization parameters for land surface temperature.\n",
        "wind_vis_params = {\n",
        "    'min': -14, 'max': 14,\n",
        "    'palette': ['orange', 'yellow', 'green', 'yellow', 'orange']}\n",
        "\n",
        "rgbVis = {\n",
        "    'min': 0.0, 'max': 3000,\n",
        "    'palette': ['orange', 'yellow', 'green', 'yellow', 'orange']\n",
        "    }\n",
        "\n",
        "# Arrange layers inside a list (elevation, LST and land cover).\n",
        "ee_tiles = [elv_img, lst_img, lc_img]#, landslid]\n",
        "\n",
        "# Arrange visualization parameters inside a list.\n",
        "ee_vis_params = [elv_vis_params, lst_vis_params, lc_vis_params]#, rgbVis]\n",
        "\n",
        "# Arrange layer names inside a list.\n",
        "ee_tiles_names = ['Elevation', 'Land Surface Temperature', 'Land Cover']#, 'Landslide']\n",
        "\n",
        "# Create a new map.\n",
        "lat, lon = u_lon, u_lat\n",
        "my_map = folium.Map(location=[lat, lon], zoom_start=10)\n",
        "\n",
        "# Add layers to the map using a loop.\n",
        "for tile, vis_param, name in zip(ee_tiles, ee_vis_params, ee_tiles_names):\n",
        "    my_map.add_ee_layer(tile, vis_param, name)\n",
        "\n",
        "folium.LayerControl(collapsed = False).add_to(my_map)\n",
        "\n",
        "folium.Marker(location=[u_lat2, u_lon2],popup='Field location',tooltip='Click here to see Popup').add_to(my_map)\n",
        "folium.Marker(location=[lat, lon],popup='New process plant location',tooltip='Click here to see Popup').add_to(my_map)\n",
        "\n",
        "my_map"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aLuiQBvnNmht"
      },
      "source": [
        "## Battery limits"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G6xIyGu-QfD5"
      },
      "source": [
        "...."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IJWp26K2P5or"
      },
      "source": [
        "## Operarion model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JoJutiZaP8WF"
      },
      "source": [
        ".....\n",
        "PE target"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0t-vboYrQj6c"
      },
      "outputs": [],
      "source": [
        "PEtarget = 0.95\n",
        "turnaroundCycle = 3 #3 years\n",
        "turnaroundSuration = 14 #days"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e3aart2cNpu6"
      },
      "source": [
        "...."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PHjO5eu_O32o"
      },
      "source": [
        "## Design life"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Uze3MttKO6nm"
      },
      "outputs": [],
      "source": [
        "designlife = 30 #years"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PM3xPnu3MGju"
      },
      "source": [
        "## Production efficiency (PE)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b5HjS86jMLK4"
      },
      "source": [
        "\n",
        "https://www.iso.org/obp/ui/#iso:std:iso:ts:3250:ed-1:v1:en\n",
        "\n",
        "Production efficiency (PE) is a term often used by operators for historic production availability in the operating phase. PE is a reported measure, and it can be compared with the predicted (or targeted) production availability made during a project development stage. Furthermore, PE is forecasted and tracked during the operating phase to allow tracking of performance. ISO 20815:2018 addresses production assurance activities including analytical methods for predicting production availability, and also includes a production loss categorization.\n",
        "PE = ratio of production to production potential (3.1.43) over a specified period of time"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nLnMx3Ttq_XF"
      },
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fefSHgO44HUH"
      },
      "source": [
        "# 4. Fluids and characterization"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I7pHEcF64PZl"
      },
      "source": [
        "## fluid compostion"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_vWsNeXQ4wLA",
        "outputId": "6cf024e6-d5bf-449c-f12b-f6a5edefefdc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "| 0                  | 1           | 2           | 3   | 4   | 5   | 6               |\n",
            "|:-------------------|:------------|:------------|:----|:----|:----|:----------------|\n",
            "|                    | total       | OIL         |     |     |     |                 |\n",
            "| nitrogen           | 5.99392E-3  | 5.99392E-3  |     |     |     | [mole fraction] |\n",
            "| CO2                | 7.22536E-5  | 7.22536E-5  |     |     |     | [mole fraction] |\n",
            "| methane            | 6.57167E-1  | 6.57167E-1  |     |     |     | [mole fraction] |\n",
            "| ethane             | 8.33727E-2  | 8.33727E-2  |     |     |     | [mole fraction] |\n",
            "| propane            | 5.0386E-2   | 5.0386E-2   |     |     |     | [mole fraction] |\n",
            "| i-butane           | 9.60861E-3  | 9.60861E-3  |     |     |     | [mole fraction] |\n",
            "| n-butane           | 1.89724E-2  | 1.89724E-2  |     |     |     | [mole fraction] |\n",
            "| i-pentane          | 7.01777E-3  | 7.01777E-3  |     |     |     | [mole fraction] |\n",
            "| n-pentane          | 8.24215E-3  | 8.24215E-3  |     |     |     | [mole fraction] |\n",
            "| n-hexane           | 9.1763E-3   | 9.1763E-3   |     |     |     | [mole fraction] |\n",
            "| C7_PC              | 1.21178E-2  | 1.21178E-2  |     |     |     | [mole fraction] |\n",
            "| C8_PC              | 1.26036E-2  | 1.26036E-2  |     |     |     | [mole fraction] |\n",
            "| C9_PC              | 7.62945E-3  | 7.62945E-3  |     |     |     | [mole fraction] |\n",
            "| C10-C11_PC         | 1.51309E-2  | 1.51309E-2  |     |     |     | [mole fraction] |\n",
            "| C12-C13_PC         | 1.31538E-2  | 1.31538E-2  |     |     |     | [mole fraction] |\n",
            "| C14-C15_PC         | 1.14407E-2  | 1.14407E-2  |     |     |     | [mole fraction] |\n",
            "| C16-C18_PC         | 1.44386E-2  | 1.44386E-2  |     |     |     | [mole fraction] |\n",
            "| C19-C20_PC         | 8.08192E-3  | 8.08192E-3  |     |     |     | [mole fraction] |\n",
            "| C21-C23_PC         | 1.02052E-2  | 1.02052E-2  |     |     |     | [mole fraction] |\n",
            "| C24-C29_PC         | 1.50362E-2  | 1.50362E-2  |     |     |     | [mole fraction] |\n",
            "| C30_PC             | 3.0153E-2   | 3.0153E-2   |     |     |     | [mole fraction] |\n",
            "|                    |             |             |     |     |     |                 |\n",
            "| Density            |             | 6.52559E2   |     |     |     | [kg/m^3]        |\n",
            "| PhaseFraction      |             | 1E0         |     |     |     | [mole fraction] |\n",
            "| MolarMass          | 6.15705E1   | 6.15705E1   |     |     |     | [kg/kmol]       |\n",
            "| Z factor           |             | 2.86201E0   |     |     |     | [-]             |\n",
            "| Heat Capacity (Cp) |             | 2.44686E0   |     |     |     | [kJ/kg*K]       |\n",
            "| Heat Capacity (Cv) |             | 2.15382E0   |     |     |     | [kJ/kg*K]       |\n",
            "| Speed of Sound     |             | 1.17248E3   |     |     |     | [m/sec]         |\n",
            "| Enthalpy           | -1.97718E1  | -1.97718E1  |     |     |     | [kJ/kg]         |\n",
            "| Entropy            | -5.91818E-1 | -5.91818E-1 |     |     |     | [kJ/kg*K]       |\n",
            "| JT coefficient     |             | -4.54486E-2 |     |     |     | [K/bar]         |\n",
            "|                    |             |             |     |     |     |                 |\n",
            "| Viscosity          |             | 2.02343E-4  |     |     |     | [kg/m*sec]      |\n",
            "| Conductivity       |             | 1.19814E-1  |     |     |     | [W/m*K]         |\n",
            "| SurfaceTension     |             |             |     |     |     | [N/m]           |\n",
            "|                    |             |             |     |     |     |                 |\n",
            "|                    |             |             |     |     |     |                 |\n",
            "|                    |             |             |     |     |     |                 |\n",
            "| Pressure           |             | 907.0       |     |     |     | [bar]           |\n",
            "| Temperature        |             | 373.15      |     |     |     | [K]             |\n",
            "|                    |             |             |     |     |     |                 |\n",
            "| Model              |             | SRK-EOS     |     |     |     | -               |\n",
            "| Mixing Rule        |             | classic     |     |     |     | -               |\n",
            "|                    |             |             |     |     |     |                 |\n",
            "| Stream             |             |             |     |     |     | -               |\n",
            "|                    |             |             |     |     |     |                 |\n",
            "|                    |             |             |     |     |     |                 |\n",
            "|                    |             |             |     |     |     |                 |\n",
            "|                    |             |             |     |     |     |                 |\n",
            "viscosity  0.20234312999290346\n",
            "API oil  31.105992916664263\n",
            "std viscosity oil  25.144731282847196 cP\n",
            "GOR  388.7032921212601  Sm3 gas / Sm3 oil\n"
          ]
        }
      ],
      "source": [
        "nitrogen = 0.599\n",
        "CO2 = 0.51\n",
        "methane = 62.794\n",
        "ethane = 8.124\n",
        "propane =  4.952\n",
        "ibutane =  1.25\n",
        "nbutane =  1.25\n",
        "ipentane =  0.25\n",
        "npentane = 0.25\n",
        "nhexane =  0.8125\n",
        "C7 =  10.05\n",
        "C7Molarmass =  218.0\n",
        "C7Density =  850.2\n",
        "\n",
        "\n",
        "resfluidcomposition = {'ComponentName':  [\"nitrogen\", \"CO2\", \"methane\", \"ethane\", \"propane\", \"i-butane\", \"n-butane\", \"i-pentane\", \"n-pentane\", \"n-hexane\", \"C7\", \"C8\", \"C9\", \"C10-C11\", \"C12-C13\", \"C14-C15\", \"C16-C18\", \"C19-C20\", \"C21-C23\", \"C24-C29\", \"C30\"],\n",
        "        'MolarComposition[-]':  [0.5948, 0.00717, 65.2132, 8.2734, 5.0, 0.9535, 1.8827, 0.6964, 0.8179, 0.9106,      1.2025, 1.2507, 0.7571, 1.5015, 1.3053, 1.1353, 1.4328, 0.802, 1.0127, 1.4921, 2.9922],\n",
        "        'MolarMass[kg/mol]': [None,None, None,None,None,None,None,None,None,None,0.09832, 0.11227, 0.12627, 0.14689, 0.174, 0.202, 0.237, 0.272, 0.307, 0.367, 0.594],\n",
        "        'RelativeDensity[-]': [None,None, None,None,None,None,None,None,None,None, 0.737, 0.758, 0.775, 0.794, 0.814, 0.830, 0.846, 0.860, 0.872, 0.889, 0.935]\n",
        "  }\n",
        "\n",
        "#fluidcomposition = {'ComponentName':  [\"nitrogen\", \"CO2\", \"methane\", \"ethane\", \"propane\", \"i-butane\", \"n-butane\", \"i-pentane\", \"n-pentane\", \"n-hexane\", \"C7\", \"C8\", \"C9\", \"C10-C11\", \"C12-C13\", \"C14-C15\", \"C16-C18\", \"C19-C20\", \"C21-C23\", \"C24-C29\", \"C30\"],\n",
        "#      'MolarComposition[-]':  [0.5901, 0.00704, 64.423, 8.0634, 4.8112, 0.9095, 1.7896, 0.6577, 0.7712, 0.8545, 1.2025, 1.2507, 0.7571, 1.5015, 1.3053, 1.1353, 1.4328, 0.802, 1.0127, 1.4921, 2.9922],\n",
        "#      'MolarMass[kg/mol]': [None,None, None,None,None,None,None,None,None,None,0.09832, 0.11227, 0.12627, 0.14689, 0.174, 0.202, 0.237, 0.272, 0.307, 0.367, 0.594],\n",
        "#      'RelativeDensity[-]': [None,None, None,None,None,None,None,None,None,None, 0.737, 0.758, 0.775, 0.794, 0.814, 0.830, 0.846, 0.860, 0.872, 0.889, 0.935]\n",
        "#}\n",
        "fluidcompositiondf = pd.DataFrame(resfluidcomposition)\n",
        "fluid1 = fluid_df(fluidcompositiondf, lastIsPlusFraction=False)\n",
        "fluid1.setMixingRule('classic')\n",
        "fluid1.setMultiPhaseCheck(True)\n",
        "\n",
        "reservoirPressure = 907\n",
        "fluid1.setPressure(reservoirPressure, 'bara')\n",
        "reservoirTemperature = 100.0 # C\n",
        "fluid1.setTemperature(reservoirTemperature, 'C')\n",
        "\n",
        "TPflash(fluid1)\n",
        "\n",
        "printFrame(fluid1)\n",
        "\n",
        "print('viscosity ', fluid1.getViscosity('cP'))\n",
        "\n",
        "\n",
        "standardPressure = 1.01325\n",
        "fluid1.setPressure(standardPressure, 'bara')\n",
        "standardTemperature = 20.0 # C\n",
        "fluid1.setTemperature(standardTemperature, 'C')\n",
        "\n",
        "TPflash(fluid1)\n",
        "fluid1.initProperties()\n",
        "stdoildensity = fluid1.getPhase('oil').getDensity('kg/m3')\n",
        "stdviscosity = fluid1.getPhase('oil').getViscosity('cP')\n",
        "stdGOR = fluid1.getPhase('gas').getVolume('m3')/fluid1.getPhase('oil').getVolume('m3')\n",
        "print('API oil ', (141.5/(stdoildensity/1e3)-131.5))\n",
        "print('std viscosity oil ', stdviscosity, 'cP')\n",
        "print('GOR ', stdGOR, ' Sm3 gas / Sm3 oil')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "06JjMG8EOmHt",
        "outputId": "ae7e131e-7e4f-4c36-bea0-1953d155eda9"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "thermoOps = jNeqSim.thermodynamicOperations.ThermodynamicOperations(fluid1.clone())\n",
        "thermoOps.calcPTphaseEnvelope()\n",
        "\n",
        "\n",
        "plt.plot(list(thermoOps.getOperation().get(\"dewT\")),list(thermoOps.getOperation().get(\"dewP\")), label=\"dew point\")\n",
        "plt.plot(list(thermoOps.getOperation().get(\"bubT\")),list(thermoOps.getOperation().get(\"bubP\")), label=\"bubble point\")\n",
        "plt.plot(list(thermoOps.getOperation().get(\"dewT2\")),list(thermoOps.getOperation().get(\"dewP2\")), label=\"dew point\")\n",
        "plt.plot(list(thermoOps.getOperation().get(\"bubT2\")),list(thermoOps.getOperation().get(\"bubP2\")), label=\"bubble point\")\n",
        "plt.title('PT envelope')\n",
        "plt.xlabel('Temperature [K]')\n",
        "plt.ylabel('Pressure [bar]')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YWLQqhOK6dFd"
      },
      "source": [
        "Calculation of saturation pressure at reservoir temperature"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1tnekVcG6iJj",
        "outputId": "5b1d527e-afc9-40c5-9d86-e0f267eaf15d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "saturation pressure  577.7127513885498  [bara]\n"
          ]
        }
      ],
      "source": [
        "from neqsim import jNeqSim\n",
        "\n",
        "reservoirTemperature = 100.0 # C\n",
        "fluid1.setTemperature(reservoirTemperature, 'C')\n",
        "satSim = jNeqSim.PVTsimulation.simulation.SaturationPressure(fluid1.clone())\n",
        "satSim.run()\n",
        "print('saturation pressure ', satSim.getSaturationPressure(), ' [bara]')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-AQguE-wXs_w"
      },
      "source": [
        "## Formation Water\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8BmwSY0TXx4V"
      },
      "outputs": [],
      "source": [
        "na = 0.1 #.."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UzN049aeXtZg"
      },
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-w8z6dzBYKYW"
      },
      "source": [
        "## Fluid characterization"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "X1aUuVwdYUkO",
        "outputId": "c1cfe754-3043-45a5-d209-5e2706a51d8c"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 2000x500 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from neqsim.thermo.thermoTools import *\n",
        "\n",
        "pressure = [900.0, 800.0, 700.0, 600.0, 500.0, 400.0, 300.0, 200.0, 100.0, 1.01325]\n",
        "temperature = [273.15+100.0]*10\n",
        "GORdata = []\n",
        "Bo = []\n",
        "GOR(fluid1.clone(), pressure, temperature, GORdata, Bo)\n",
        "\n",
        "plt.figure(figsize=(20, 5))\n",
        "plt.subplot(131)\n",
        "plt.plot(pressure, GORdata, \"o\")\n",
        "plt.xlabel('Pressure [bara]')\n",
        "plt.ylabel('GORdata [Sm3 gas/Sm3 oil]')\n",
        "plt.subplot(132)\n",
        "plt.plot(pressure, Bo, \"o\")\n",
        "plt.xlabel('Pressure [bara]')\n",
        "plt.ylabel('Bo[m3/Sm3]')\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 932
        },
        "id": "v6zL69cv7h_2",
        "outputId": "99d3a63d-8eb0-4ec1-dc0f-fa187b5cde78"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 0 Axes>"
            ]
          },
          "execution_count": 40,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 0 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "gasviscosity = []\n",
        "oilviscosity = []\n",
        "\n",
        "pressure = [900.0, 800.0, 700.0, 600, 500.0, 400.0, 300.0, 200.0, 100.0, 1.01325]\n",
        "temperature = [373.15]*10\n",
        "\n",
        "viscositysim(fluid1.clone(),pressure,temperature,gasviscosity,oilviscosity)\n",
        "\n",
        "plt.figure()\n",
        "plt.plot(pressure, gasviscosity, \"o\")\n",
        "plt.xlabel('Pressure [bara]')\n",
        "plt.ylabel('gasviscosity [kg/msec]')\n",
        "plt.figure()\n",
        "plt.plot(pressure, oilviscosity, \"o\")\n",
        "plt.xlabel('Pressure [bara]')\n",
        "plt.ylabel('oilviscosity [kg/msec]')\n",
        "plt.figure()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4h_pAPIoYv1a"
      },
      "source": [
        "#5. Reservoir simulations"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "uPoRoxzKY0I6",
        "outputId": "4726504b-516c-4ae8-9b91-d6237fba0e07"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "gas in place (GIP)  206.7059730775122  GSm3\n",
            "oil in place (OIP)  538.1164512982363  MSm3\n",
            "gas production  9.770017551516737  MSm3/day\n",
            "oil production  25446.98813258201  Sm3/day\n",
            "oil production  160061.55535394084  bbl/day\n",
            "water production  22.242652194075493  Sm3/hr\n",
            "gas production  9.86031249979216  MSm3/day\n",
            "oil production  25430.15215456133  Sm3/day\n",
            "oil production  159955.65705219077  bbl/day\n",
            "water production  22.345658360260092  Sm3/hr\n",
            "gas production  9.284735468170714  MSm3/day\n",
            "oil production  23721.142146968366  Sm3/day\n",
            "oil production  149205.98410443103  bbl/day\n",
            "water production  22.398098333588187  Sm3/hr\n",
            "gas production  8.828160954186814  MSm3/day\n",
            "oil production  22342.412707639676  Sm3/day\n",
            "oil production  140533.77593105356  bbl/day\n",
            "water production  22.434285208001313  Sm3/hr\n",
            "gas production  8.459639683443282  MSm3/day\n",
            "oil production  21207.72639764968  Sm3/day\n",
            "oil production  133396.59904121648  bbl/day\n",
            "water production  22.45933026016521  Sm3/hr\n",
            "oil production  total  41.56568006335695  MSm3 oe\n",
            "oil production  total  261.4481275985152  Mboe oe\n",
            "reservoir recovery  5.927609792060937  %\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-6-f4ef59b96497>:105: MatplotlibDeprecationWarning: Auto-removal of overlapping axes is deprecated since 3.6 and will be removed two minor releases later; explicitly call ax.remove() as needed.\n",
            "  plt.subplot(7, 1, 1)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'CO2 in reservoir gas [mole frac]')"
            ]
          },
          "metadata": {},
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x2000 with 7 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "from neqsim.process.processTools import simplereservoir\n",
        "\n",
        "resFLuid = fluid1.clone()\n",
        "resFLuid.addComponent(\"water\", 15.0)\n",
        "resFLuid.setMixingRule(2)\n",
        "\n",
        "resFLuid.setPressure(initialReservoirPressure, 'bara')\n",
        "resFLuid.setTemperature(initialReservoirTemperature,'C')\n",
        "TPflash(resFLuid)\n",
        "\n",
        "reservoirOps = simplereservoir(resFLuid, \"Well 1 reservoir\", gasvolume=0.0, oilvolume=OOIP/recoveryFactor, watervolume=10.0e7)\n",
        "\n",
        "\n",
        "#oil producer wells\n",
        "producedOilStream = reservoirOps.addOilProducer(\"SLP_A32562O\")\n",
        "producedOilStream.setFlowRate(6500.0 / 0.86 * 1000.0, \"kg/day\") #max rate producer 6500 m3/day 6 producers\n",
        "\n",
        "producedOilStream2 = reservoirOps.addOilProducer(\"SLP_A325621\")\n",
        "producedOilStream2.setFlowRate(6500.0 / 0.86 * 1000.0, \"kg/day\")\n",
        "\n",
        "producedOilStream3 = reservoirOps.addOilProducer(\"SLP_A325622\")\n",
        "producedOilStream3.setFlowRate(6500.0 / 0.86 * 1000.0, \"kg/day\")\n",
        "\n",
        "producedOilStream4 = reservoirOps.addOilProducer(\"SLP_A3256222\")\n",
        "producedOilStream4.setFlowRate(6500.0 / 0.86 * 1000.0, \"kg/day\")\n",
        "\n",
        "\n",
        "#Gas Injection Wells\n",
        "injectionGasStream = reservoirOps.addGasInjector(\"SLP_A32562GI\")\n",
        "injgascomp = [0.0]*22\n",
        "injgascomp[2] = 1.0\n",
        "fluidComposition(injectionGasStream.getFluid(), injgascomp)\n",
        "injectionGasStream.setFlowRate(5.0, \"MSm3/day\") # Max rate for gas injextor 5 MSm3/day\n",
        "\n",
        "injectionGasStream2 = reservoirOps.addGasInjector(\"SLP_A32566GI\")\n",
        "fluidComposition(injectionGasStream.getFluid(), injgascomp)\n",
        "injectionGasStream2.setFlowRate(0.0, \"MSm3/day\") # Max rate for gas injextor 5 MSm3/day\n",
        "\n",
        "\n",
        "#Water injector wells\n",
        "injectionWaterStream = reservoirOps.addWaterInjector(\"SLP_A32562W\")\n",
        "injectionWaterStream.setFlowRate(8000.0 * 1000, \"kg/day\") #max rate water injector 8000 m3/day 1 injetors\n",
        "\n",
        "\n",
        "# Water producer\n",
        "producedWaterStream = reservoirOps.addWaterProducer(\"prod water 1\")\n",
        "producedWaterStream.setFlowRate(100000.0, \"kg/day\")\n",
        "\n",
        "producedWaterStream2 = reservoirOps.addWaterProducer(\"prod water 2\")\n",
        "producedWaterStream2.setFlowRate(90000.0, \"kg/day\")\n",
        "\n",
        "producedWaterStream3 = reservoirOps.addWaterProducer(\"prod water 3\")\n",
        "producedWaterStream3.setFlowRate(121100.0, \"kg/day\")\n",
        "\n",
        "producedWaterStream4 = reservoirOps.addWaterProducer(\"prod water 4\")\n",
        "producedWaterStream4.setFlowRate(90000.0, \"kg/day\")\n",
        "\n",
        "\n",
        "deltat = 24*60*60.0*365\n",
        "time = []\n",
        "pressure = []\n",
        "gasvolume = []\n",
        "watervolume = []\n",
        "oilvolume = []\n",
        "wateringas = []\n",
        "totalproducedoe = []\n",
        "co2reservoirgas = []\n",
        "\n",
        "reservoirOps.run()\n",
        "\n",
        "print(\"gas in place (GIP) \", reservoirOps.getGasInPlace(\"GSm3\"), \" GSm3\")\n",
        "print(\"oil in place (OIP) \", reservoirOps.getOilInPlace(\"MSm3\"), \" MSm3\")\n",
        "\n",
        "for t in range(5):\n",
        "    time.append(t*deltat/(24*60*60.0*365))\n",
        "    pressure.append(reservoirOps.getReservoirFluid().getPressure('bara'))\n",
        "    try:\n",
        "      gasvolume.append(reservoirOps.getReservoirFluid().getPhase(\"gas\").getVolume(\"m3\"))\n",
        "      wateringas.append(reservoirOps.getReservoirFluid().getPhase(\"gas\").getComponent('water').getx())\n",
        "    except:\n",
        "      gasvolume.append(0)\n",
        "      wateringas.append(0)\n",
        "    oilvolume.append(reservoirOps.getReservoirFluid().getPhase(\"oil\").getVolume(\"m3\"))\n",
        "    watervolume.append(reservoirOps.getReservoirFluid().getPhase(\"aqueous\").getVolume(\"m3\"))\n",
        "    totalproducedoe.append(reservoirOps.getProductionTotal('MSm3 oe'))\n",
        "    co2reservoirgas.append(reservoirOps.getReservoirFluid().getComponent('CO2').getz())\n",
        "    print('gas production ' , reservoirOps.getGasProdution('Sm3/day')/1e6, ' MSm3/day')\n",
        "    print('oil production ' , reservoirOps.getOilProdution('Sm3/day'), ' Sm3/day')\n",
        "    print('oil production ' , reservoirOps.getOilProdution('Sm3/day')*6.29, ' bbl/day')\n",
        "    print('water production ' , reservoirOps.getWaterProdution('Sm3/hr'), ' Sm3/hr')\n",
        "    producedOilStream.setFlowRate(6500.0 / 0.86 * 1000.0 * reservoirOps.getReservoirFluid().getPressure('bara')/900.0, \"kg/day\")\n",
        "    producedOilStream2.setFlowRate(6500.0 / 0.86 * 1000.0 * reservoirOps.getReservoirFluid().getPressure('bara')/900.0, \"kg/day\")\n",
        "    producedOilStream3.setFlowRate(6500.0 / 0.86 * 1000.0 * reservoirOps.getReservoirFluid().getPressure('bara')/900.0, \"kg/day\")\n",
        "    producedOilStream4.setFlowRate(6500.0 / 0.86 * 1000.0 * reservoirOps.getReservoirFluid().getPressure('bara')/900.0, \"kg/day\")\n",
        "    injectionGasStream.setFlowRate(5.0, \"MSm3/day\")\n",
        "    injectionGasStream2.setFlowRate(1.0, \"MSm3/day\")\n",
        "    reservoirOps.runTransient(deltat)\n",
        "\n",
        "print(\"oil production  total \", reservoirOps.getOilProductionTotal('MSm3'), \" MSm3 oe\")\n",
        "print(\"oil production  total \", reservoirOps.getOilProductionTotal('MSm3')*6.29, \" Mboe oe\")\n",
        "print(\"reservoir recovery \", reservoirOps.getOilProductionTotal('MSm3')/(reservoirOps.getOilInPlace(\"MSm3\")+ reservoirOps.getGasInPlace(\"GSm3\"))*100.0, \" %\")\n",
        "\n",
        "plt.rcParams['figure.figsize'] = [10, 20]\n",
        "fig, ax = plt.subplots()\n",
        "plt.subplot(7, 1, 1)\n",
        "plt.plot(time, pressure)\n",
        "plt.xlabel('Year [-]')\n",
        "plt.ylabel('Pressure [bara]')\n",
        "plt.subplot(7, 1, 2)\n",
        "plt.plot(time, gasvolume)\n",
        "plt.xlabel('Year [-]')\n",
        "plt.ylabel('gasVol [m3]')\n",
        "plt.subplot(7, 1, 3)\n",
        "plt.plot(time, oilvolume)\n",
        "plt.xlabel('Year [-]')\n",
        "plt.ylabel('oilVol [m3]')\n",
        "plt.subplot(7, 1, 4)\n",
        "plt.plot(time, watervolume)\n",
        "plt.xlabel('Year [-]')\n",
        "plt.ylabel('waterVol [m3]')\n",
        "plt.subplot(7, 1, 5)\n",
        "plt.plot(time, wateringas)\n",
        "plt.xlabel('Year [-]')\n",
        "plt.ylabel('water in gas [-]')\n",
        "plt.subplot(7, 1, 6)\n",
        "plt.plot(time, totalproducedoe)\n",
        "plt.xlabel('Year [-]')\n",
        "plt.ylabel('Total production [MSm3 oe]')\n",
        "plt.subplot(7, 1, 7)\n",
        "plt.plot(time, co2reservoirgas)\n",
        "plt.xlabel('Year [-]')\n",
        "plt.ylabel('CO2 in reservoir gas [mole frac]')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d_LbU5b2Y9aO"
      },
      "source": [
        "#6. SURF and flow assurance"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8OPHcnReJSmR"
      },
      "source": [
        "## Wells and xmas tree"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YRqXna3FZJj5",
        "outputId": "e9fd8be3-02e1-43c7-93c6-3c9d32d82a34"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "prod tubing ID  16.25  cm\n",
            "flow line tubing ID  25.0  cm\n",
            "reservoir pressure  701.6049682668402  bara\n",
            "well head pressure  402.4537766838619  bara\n",
            "x-mas tree pressure  350.0  bara\n",
            "riser bottom pressure  330.46959676427906  bara\n",
            "pressure to FPSO  184.56649935185408  bara\n",
            "superficial velocity  2.8973793021073595  m/sec\n",
            "liquid holdup of FPSO feed  0.625815403279972  [-]\n",
            "gas feed to process  4.829251335869121 MSm3/day\n",
            "oil feed to process  1389.3290687936114 m3/hr\n",
            "water feed to process  132.49174607472156 m3/hr\n",
            "| 0                  | 1           | 2                  | 3                  | 4                  | 5   | 6               |\n",
            "|:-------------------|:------------|:-------------------|:-------------------|:-------------------|:----|:----------------|\n",
            "|                    | total       | GAS                | OIL                | AQUEOUS            |     |                 |\n",
            "| nitrogen           | 3.6526E-3   | 8.12029E-3         | 1.88107E-3         | 3.28762E-7         |     | [mole fraction] |\n",
            "| CO2                | 4.39505E-5  | 7.42638E-5         | 4.87521E-5         | 3.75373E-7         |     | [mole fraction] |\n",
            "| methane            | 4.24326E-1  | 8.2681E-1          | 3.49989E-1         | 4.61406E-5         |     | [mole fraction] |\n",
            "| ethane             | 5.09801E-2  | 7.73068E-2         | 6.6907E-2          | 3.73772E-7         |     | [mole fraction] |\n",
            "| propane            | 3.11261E-2  | 3.74452E-2         | 5.18557E-2         | 1.58554E-9         |     | [mole fraction] |\n",
            "| i-butane           | 6.05368E-3  | 6.10646E-3         | 1.14124E-2         | 4.85025E-12        |     | [mole fraction] |\n",
            "| n-butane           | 1.21537E-2  | 1.09843E-2         | 2.43509E-2         | 1.12277E-11        |     | [mole fraction] |\n",
            "| i-pentane          | 4.78522E-3  | 3.46032E-3         | 1.05629E-2         | 1.06413E-13        |     | [mole fraction] |\n",
            "| n-pentane          | 5.79083E-3  | 3.8637E-3          | 1.3148E-2          | 7.91528E-14        |     | [mole fraction] |\n",
            "| n-hexane           | 7.39268E-3  | 3.5859E-3          | 1.83042E-2         | 7.78608E-16        |     | [mole fraction] |\n",
            "| C7_PC              | 1.11159E-2  | 3.27479E-3         | 2.99113E-2         | 6.75808E-12        |     | [mole fraction] |\n",
            "| C8_PC              | 1.18485E-2  | 2.60227E-3         | 3.2885E-2          | 1.37368E-13        |     | [mole fraction] |\n",
            "| C9_PC              | 7.23274E-3  | 1.19265E-3         | 2.05207E-2         | 1.66838E-15        |     | [mole fraction] |\n",
            "| C10-C11_PC         | 1.43933E-2  | 1.5699E-3          | 4.1743E-2          | 1.19517E-17        |     | [mole fraction] |\n",
            "| C12-C13_PC         | 1.25226E-2  | 7.83076E-4         | 3.69752E-2         | 1.24923E-20        |     | [mole fraction] |\n",
            "| C14-C15_PC         | 1.08929E-2  | 3.74634E-4         | 3.2509E-2          | 2.17471E-23        |     | [mole fraction] |\n",
            "| C16-C18_PC         | 1.37475E-2  | 2.14794E-4         | 4.13195E-2         | 3.22446E-26        |     | [mole fraction] |\n",
            "| C19-C20_PC         | 7.69513E-3  | 5.19134E-5         | 2.32056E-2         | 6.79079E-29        |     | [mole fraction] |\n",
            "| C21-C23_PC         | 9.7167E-3   | 2.73371E-5         | 2.9345E-2          | 7.63646E-31        |     | [mole fraction] |\n",
            "| C24-C29_PC         | 1.43164E-2  | 8.60813E-6         | 4.32722E-2         | 1.51796E-33        |     | [mole fraction] |\n",
            "| C30_PC             | 2.87097E-2  | 7.02838E-8         | 8.67959E-2         | 7.75397E-40        |     | [mole fraction] |\n",
            "| water              | 3.11504E-1  | 1.21429E-2         | 3.30577E-2         | 9.99953E-1         |     | [mole fraction] |\n",
            "|                    |             |                    |                    |                    |     |                 |\n",
            "| Density            |             | 1.32255E2          | 7.42932E2          | 9.17973E2          |     | [kg/m^3]        |\n",
            "| PhaseFraction      |             | 3.73176E-1         | 3.30772E-1         | 2.96052E-1         |     | [mole fraction] |\n",
            "| MolarMass          | 5.83593E1   | 2.08046E1          | 1.36838E2          | 1.80149E1          |     | [kg/kmol]       |\n",
            "| Z factor           |             | 9.1885E-1          | 1.10754E0          | 1.47233E-1         |     | [-]             |\n",
            "| Heat Capacity (Cp) |             | 3.06856E0          | 2.38599E0          | 4.83613E0          |     | [kJ/kg*K]       |\n",
            "| Heat Capacity (Cv) |             | 2.03474E0          | 2.17084E0          | 3.22333E0          |     | [kJ/kg*K]       |\n",
            "| Speed of Sound     |             | 4.77462E2          | 8.65716E2          | 2.60017E3          |     | [m/sec]         |\n",
            "| Enthalpy           | -2.51366E2  | 1.07721E2          | -9.11292E1         | -2.13396E3         |     | [kJ/kg]         |\n",
            "| Entropy            | -7.13844E-1 | -1.32247E0         | -2.63422E-2        | -5.66244E0         |     | [kJ/kg*K]       |\n",
            "| JT coefficient     |             | 1.5437E-1          | -3.82451E-2        | -1.83914E-2        |     | [K/bar]         |\n",
            "|                    |             |                    |                    |                    |     |                 |\n",
            "| Viscosity          |             | 1.95069E-5         | 4.68102E-4         | 2.60254E-4         |     | [kg/m*sec]      |\n",
            "| Conductivity       |             | 5.89826E-2         | 1.07184E-1         | 6.92626E-1         |     | [W/m*K]         |\n",
            "| SurfaceTension     |             | 2.44637E-2         | 2.44637E-2         |                    |     | [N/m]           |\n",
            "|                    |             |                    | 9.93665E0          | 9.93665E0          |     | [N/m]           |\n",
            "|                    |             | 2.77041E0          |                    | 2.77041E0          |     | [N/m]           |\n",
            "|                    |             |                    |                    |                    |     |                 |\n",
            "| Pressure           |             | 184.56649935185408 | 184.56649935185408 | 184.56649935185408 |     | [bar]           |\n",
            "| Temperature        |             | 383.85826262496823 | 383.85826262496823 | 383.85826262496823 |     | [K]             |\n",
            "|                    |             |                    |                    |                    |     |                 |\n",
            "| Model              |             | SRK-EOS            | SRK-EOS            | SRK-EOS            |     | -               |\n",
            "| Mixing Rule        |             | classic            | classic            | classic            |     | -               |\n",
            "|                    |             |                    |                    |                    |     |                 |\n",
            "| Stream             |             |                    |                    |                    |     | -               |\n",
            "|                    |             |                    |                    |                    |     |                 |\n",
            "|                    |             |                    |                    |                    |     |                 |\n",
            "|                    |             |                    |                    |                    |     |                 |\n",
            "|                    |             |                    |                    |                    |     |                 |\n"
          ]
        }
      ],
      "source": [
        "from neqsim.process import mixer, valve\n",
        "\n",
        "prodtubingID = 6.5*0.025 # meter 7\" OD\n",
        "print('prod tubing ID ', prodtubingID*100, ' cm')\n",
        "\n",
        "flowline1ID = 10*0.025\n",
        "print('flow line tubing ID ', flowline1ID*100, ' cm')\n",
        "\n",
        "well1mix = mixer('well1mixer')\n",
        "well1mix.addStream(producedOilStream)\n",
        "well1mix.addStream(producedWaterStream)\n",
        "well1mix.run()\n",
        "\n",
        "well2mix = mixer('well2mixer')\n",
        "well2mix.addStream(producedOilStream2)\n",
        "well2mix.addStream(producedWaterStream2)\n",
        "well2mix.run()\n",
        "\n",
        "well3mix = mixer('well3mixer')\n",
        "well3mix.addStream(producedOilStream3)\n",
        "well3mix.addStream(producedWaterStream3)\n",
        "well3mix.run()\n",
        "\n",
        "well4mix = mixer('well3mixer')\n",
        "well4mix.addStream(producedOilStream4)\n",
        "well4mix.addStream(producedWaterStream4)\n",
        "well4mix.run()\n",
        "\n",
        "well1 = jNeqSim.processSimulation.processEquipment.pipeline.PipeBeggsAndBrills(well1mix.getOutStream());\n",
        "well1.setDiameter(prodtubingID);\n",
        "well1.setPipeWallRoughness(15e-6);\n",
        "well1.setLength(4000);\n",
        "well1.setElevation(4000.0);\n",
        "well1.setNumberOfIncrements(5);\n",
        "well1.run()\n",
        "\n",
        "well2 = jNeqSim.processSimulation.processEquipment.pipeline.PipeBeggsAndBrills(well2mix.getOutStream());\n",
        "well2.setDiameter(prodtubingID);\n",
        "well2.setPipeWallRoughness(15e-6);\n",
        "well2.setLength(4000);\n",
        "well2.setElevation(4000.0);\n",
        "well2.setNumberOfIncrements(5);\n",
        "well2.run()\n",
        "\n",
        "well3 = jNeqSim.processSimulation.processEquipment.pipeline.PipeBeggsAndBrills(well3mix.getOutStream());\n",
        "well3.setDiameter(prodtubingID);\n",
        "well3.setPipeWallRoughness(15e-6);\n",
        "well3.setLength(4000);\n",
        "well3.setElevation(4000.0);\n",
        "well3.setNumberOfIncrements(5);\n",
        "well3.run()\n",
        "\n",
        "well4 = jNeqSim.processSimulation.processEquipment.pipeline.PipeBeggsAndBrills(well4mix.getOutStream());\n",
        "well4.setDiameter(prodtubingID);\n",
        "well4.setPipeWallRoughness(15e-6);\n",
        "well4.setLength(4000);\n",
        "well4.setElevation(4000.0);\n",
        "well4.setNumberOfIncrements(5);\n",
        "well4.run()\n",
        "\n",
        "subseachoke1 = valve(well1.getOutletStream())\n",
        "subseachoke1.setName('choke 1')\n",
        "subseachoke1.setOutletPressure(flowlinemaxpressure, 'bara')\n",
        "\n",
        "subseachoke2 = valve(well2.getOutletStream())\n",
        "subseachoke2.setName('choke 2')\n",
        "subseachoke2.setOutletPressure(flowlinemaxpressure, 'bara')\n",
        "\n",
        "subseachoke3 = valve(well3.getOutletStream())\n",
        "subseachoke3.setName('choke 3')\n",
        "subseachoke3.setOutletPressure(flowlinemaxpressure, 'bara')\n",
        "\n",
        "subseachoke4 = valve(well4.getOutletStream())\n",
        "subseachoke4.setName('choke 4')\n",
        "subseachoke4.setOutletPressure(flowlinemaxpressure, 'bara')\n",
        "\n",
        "\n",
        "template1 = mixer('template 1')\n",
        "template1.addStream(subseachoke1.getOutStream())\n",
        "template1.addStream(subseachoke2.getOutStream())\n",
        "template1.addStream(subseachoke3.getOutStream())\n",
        "template1.addStream(subseachoke4.getOutStream())\n",
        "template1.run()\n",
        "import jpype\n",
        "from jpype.types import *\n",
        "flowset =  jNeqSim.processSimulation.processEquipment.util.FlowSetter(\"flowset\", template1.getOutStream())\n",
        "#pressureSep = [90.0, 22.0, 1.01325]\n",
        "#temperatureSep = [15.0, 15.0, 15.0]\n",
        "#flowset.setSeparationPT(JDouble[:](pressureSep), \"bara\", JDouble[:](temperatureSep), \"C\");\n",
        "flowset.setGasFlowRate(7.0, \"MSm3/day\")\n",
        "flowset.setOilFlowRate(1080.0, \"m3/hr\")\n",
        "flowset.setWaterFlowRate(108.0, \"m3/hr\")\n",
        "flowset.run()\n",
        "\n",
        "flowline1 = jNeqSim.processSimulation.processEquipment.pipeline.PipeBeggsAndBrills(flowset.getOutletStream());\n",
        "flowline1.setDiameter(flowline1ID*2);\n",
        "flowline1.setPipeWallRoughness(15e-6);\n",
        "flowline1.setLength(20000);\n",
        "flowline1.setElevation(0.0);\n",
        "flowline1.setNumberOfIncrements(5);\n",
        "flowline1.run()\n",
        "\n",
        "productionriser = jNeqSim.processSimulation.processEquipment.pipeline.PipeBeggsAndBrills(flowline1.getOutStream());\n",
        "productionriser.setDiameter(0.525);\n",
        "productionriser.setPipeWallRoughness(15e-6);\n",
        "productionriser.setLength(3500);\n",
        "productionriser.setElevation(2500.0);\n",
        "productionriser.setNumberOfIncrements(5);\n",
        "productionriser.run()\n",
        "\n",
        "print('reservoir pressure ', well3mix.getOutStream().getPressure('bara'), ' bara')\n",
        "print('well head pressure ', well1.getOutletStream().getPressure('bara'), ' bara')\n",
        "print('x-mas tree pressure ', template1.getOutStream().getPressure('bara'), ' bara')\n",
        "print('riser bottom pressure ', flowline1.getOutStream().getPressure('bara'), ' bara')\n",
        "print('pressure to FPSO ', productionriser.getOutStream().getPressure('bara'), ' bara')\n",
        "print('superficial velocity ' , flowline1.getSuperficialVelocity(), ' m/sec')\n",
        "print('liquid holdup of FPSO feed ', productionriser.getSegmentLiquidHoldup(5), ' [-]')\n",
        "print('gas feed to process ', productionriser.getOutStream().getFluid().getPhase('gas').getFlowRate('MSm3/day'), 'MSm3/day')\n",
        "print('oil feed to process ', productionriser.getOutStream().getFluid().getPhase('oil').getFlowRate('m3/hr'), 'm3/hr')\n",
        "print('water feed to process ', productionriser.getOutStream().getFluid().getPhase('aqueous').getFlowRate('m3/hr'), 'm3/hr')\n",
        "\n",
        "printFrame(productionriser.getOutStream().getFluid())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p0APaL2xJgBR"
      },
      "source": [
        "## Multiphase flow regime"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 599
        },
        "id": "4Fm6Y6eVG_Ob",
        "outputId": "02b3fff1-a8d0-4f7f-83b2-772b1930dfe2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "failed\n",
            "failed\n",
            "failed\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from neqsim.process import stream\n",
        "\n",
        "def define_fluid(diameter, liquid_us, gas_us):\n",
        "  area = 3.14 * diameter**2/4\n",
        "  gasFlowRate = gas_us*area\n",
        "  liquidFlowRate = liquid_us*area\n",
        "\n",
        "\n",
        "\n",
        "  gas_fluid = template1.getOutStream().getFluid().phaseToSystem(\"gas\")\n",
        "  gas_fluid.init(0)\n",
        "\n",
        "\n",
        "\n",
        "  water_fluid = template1.getOutStream().getFluid().phaseToSystem(\"aqueous\")\n",
        "  water_fluid.init(0)\n",
        "\n",
        "\n",
        "\n",
        "  gas_stream = stream(gas_fluid)\n",
        "  gas_stream.setFlowRate(gasFlowRate, \"Am3/sec\")\n",
        "  gas_stream.run()\n",
        "\n",
        "\n",
        "\n",
        "  water_stream = stream(water_fluid)\n",
        "  water_stream.setFlowRate(23.5*liquidFlowRate, \"Am3/sec\")\n",
        "  water_stream.run()\n",
        "\n",
        "\n",
        "\n",
        "  mixerStream = mixer()\n",
        "  mixerStream.addStream(gas_stream)\n",
        "  mixerStream.addStream(water_stream)\n",
        "  mixerStream.run()\n",
        "  return mixerStream.getOutStream()\n",
        "\n",
        "\n",
        "\n",
        "superficialGasVelocity = []\n",
        "superficialLiquidVelocity = []\n",
        "flowRegime = []\n",
        "\n",
        "\n",
        "\n",
        "superficialVelocityRangeGas = np.linspace(0.01, 5, 10)\n",
        "superficialVelocityRangeLiquid = np.linspace(0.01, 2.5, 10)\n",
        "\n",
        "\n",
        "\n",
        "for gas_us in superficialVelocityRangeGas:\n",
        "  for liquid_us in superficialVelocityRangeLiquid:\n",
        "    flowlineMap = jNeqSim.processSimulation.processEquipment.pipeline.PipeBeggsAndBrills(define_fluid(0.4, liquid_us, gas_us));\n",
        "    flowlineMap.setDiameter(0.40);\n",
        "    flowlineMap.setPipeWallRoughness(5e-6);\n",
        "    flowlineMap.setLength(10000);\n",
        "    flowlineMap.setElevation(3500.0);\n",
        "    flowlineMap.setNumberOfIncrements(1);\n",
        "    try:\n",
        "      flowlineMap.run()\n",
        "      superficialGasVelocity.append(list(flowlineMap.getGasSuperficialVelocityProfile())[0])\n",
        "      superficialLiquidVelocity.append(list(flowlineMap.getLiquidSuperficialVelocityProfile())[0])\n",
        "      flowRegime.append(list(flowlineMap.getFlowRegimeProfile())[0])\n",
        "    except:\n",
        "      print(\"failed\")\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "# Create a color map dictionary for flow regimes\n",
        "\n",
        "\n",
        "# Set the figure size\n",
        "plt.figure(figsize=(8, 6))  # Adjust the width and height values as needed\n",
        "\n",
        "\n",
        "\n",
        "flow_regime_colors = {\n",
        "    'TRANSITION': 'nlue',\n",
        "    'INTERMITTENT': 'blue',\n",
        "    'DISTRIBUTED': 'green',\n",
        "    'SEGREGATED': 'orange',\n",
        "    'Single Phase': 'purple'\n",
        "}\n",
        "\n",
        "\n",
        "\n",
        "# Convert flowRegime to numeric values for colormap\n",
        "flow_regime_numeric = [list(flow_regime_colors.keys()).index(regime) for regime in flowRegime]\n",
        "\n",
        "# Operational\n",
        "operational_point_liq_velocity = [0.0299052078802805, 1.3876390158714877]\n",
        "operational_point_gas_velocity = [3.779541453948589, 3.056532086170634]\n",
        "\n",
        "# Plot the scatter plot with color mapping\n",
        "plt.scatter(superficialGasVelocity, superficialLiquidVelocity, c=flow_regime_numeric, cmap='viridis')\n",
        "plt.plot(operational_point_gas_velocity, operational_point_liq_velocity, color = \"red\", marker = \"o\")\n",
        "\n",
        "\n",
        "\n",
        "# Set axis labels\n",
        "plt.xlabel('Superficial Gas Velocity [m/s]')\n",
        "plt.ylabel('Superficial Liquid Velocity [m/s]')\n",
        "\n",
        "\n",
        "\n",
        "# Create a colorbar legend\n",
        "flow_regime_labels = list(flow_regime_colors.keys())\n",
        "cbar = plt.colorbar(ticks=np.arange(len(flow_regime_labels)))\n",
        "cbar.ax.set_yticklabels(flow_regime_labels)\n",
        "\n",
        "\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dmtM1KlAiVIR"
      },
      "source": [
        "## Flow Assurance"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7c35KVjMibm4"
      },
      "source": [
        "### Hydrate formation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 561
        },
        "id": "v_sP_GTQilUB",
        "outputId": "612e39d6-742b-43f9-ff01-c2f5129f61dd"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Pressure [bara]')"
            ]
          },
          "execution_count": 48,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "fluid1 = template1.getOutStream().getFluid().clone()\n",
        "\n",
        "minPressure = 10.0\n",
        "maxPressure = 1000.0\n",
        "\n",
        "def hydrateEqTemp(pressure):\n",
        "    fluid1.setPressure(pressure)\n",
        "    hydt(fluid1)\n",
        "    return fluid1.getTemperature('C')\n",
        "\n",
        "hydpressure = np.arange(minPressure, maxPressure, 50.0)\n",
        "hydtemperature = [hydrateEqTemp(P) for P in hydpressure]\n",
        "\n",
        "plt.figure(figsize=(8, 6))  # Adjust the width and height values as needed\n",
        "plt.plot(hydtemperature, hydpressure)\n",
        "plt.xlabel('Temperature [C]')\n",
        "plt.ylabel('Pressure [bara]')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2emumCCuil-c"
      },
      "source": [
        "### Corrosion control and scaling"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LuCPNalzl8bq"
      },
      "source": [
        "to be updated with more realistic numbers numbers..."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0kaXy2COipvh",
        "outputId": "5d96592c-c714-4c13-cc7e-1293e8bd8307"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "pH of water  4.582493404067516\n",
            "| 0                  | 1          | 2                  | 3                  | 4   | 5   | 6               |\n",
            "|:-------------------|:-----------|:-------------------|:-------------------|:----|:----|:----------------|\n",
            "|                    | total      | OIL                | AQUEOUS            |     |     |                 |\n",
            "| nitrogen           | 5.40717E-3 | 5.76077E-3         | 3.19432E-8         |     |     | [mole fraction] |\n",
            "| CO2                | 6.49641E-5 | 6.92035E-5         | 1.37171E-7         |     |     | [mole fraction] |\n",
            "| methane            | 6.27923E-1 | 6.68986E-1         | 2.31212E-6         |     |     | [mole fraction] |\n",
            "| ethane             | 7.52149E-2 | 8.01337E-2         | 1.9005E-9          |     |     | [mole fraction] |\n",
            "| propane            | 4.54562E-2 | 4.84289E-2         | 3.46374E-13        |     |     | [mole fraction] |\n",
            "| i-butane           | 8.66851E-3 | 9.23539E-3         | 7.94937E-17        |     |     | [mole fraction] |\n",
            "| n-butane           | 1.71161E-2 | 1.82354E-2         | 1.80298E-16        |     |     | [mole fraction] |\n",
            "| i-pentane          | 6.33108E-3 | 6.74511E-3         | 1.6014E-19         |     |     | [mole fraction] |\n",
            "| n-pentane          | 7.43574E-3 | 7.92201E-3         | 8.49638E-20        |     |     | [mole fraction] |\n",
            "| n-hexane           | 8.27842E-3 | 8.8198E-3          | 4.02206E-23        |     |     | [mole fraction] |\n",
            "| C7_PC              | 1.09322E-2 | 1.16471E-2         | 2.34562E-17        |     |     | [mole fraction] |\n",
            "| C8_PC              | 1.13704E-2 | 1.2114E-2          | 4.32827E-20        |     |     | [mole fraction] |\n",
            "| C9_PC              | 6.883E-3   | 7.33311E-3         | 5.06896E-23        |     |     | [mole fraction] |\n",
            "| C10-C11_PC         | 1.36504E-2 | 1.45431E-2         | 1.30091E-26        |     |     | [mole fraction] |\n",
            "| C12-C13_PC         | 1.18667E-2 | 1.26428E-2         | 2.59873E-31        |     |     | [mole fraction] |\n",
            "| C14-C15_PC         | 1.03212E-2 | 1.09962E-2         | 1.18024E-35        |     |     | [mole fraction] |\n",
            "| C16-C18_PC         | 1.30259E-2 | 1.38777E-2         | 3.34577E-40        |     |     | [mole fraction] |\n",
            "| C19-C20_PC         | 7.29119E-3 | 7.76801E-3         | 2.64138E-44        |     |     | [mole fraction] |\n",
            "| C21-C23_PC         | 9.20665E-3 | 9.80873E-3         | 1.82041E-47        |     |     | [mole fraction] |\n",
            "| C24-C29_PC         | 1.35649E-2 | 1.4452E-2          | 6.93708E-52        |     |     | [mole fraction] |\n",
            "| C30_PC             | 2.72027E-2 | 2.89816E-2         | 2.25473E-62        |     |     | [mole fraction] |\n",
            "| water              | 6.27889E-2 | 1.49958E-3         | 9.99998E-1         |     |     | [mole fraction] |\n",
            "|                    |            |                    |                    |     |     |                 |\n",
            "| Density            |            | 6.83709E2          | 1.00925E3          |     |     | [kg/m^3]        |\n",
            "| PhaseFraction      |            | 9.38618E-1         | 6.13815E-2         |     |     | [mole fraction] |\n",
            "| MolarMass          | 5.72396E1  | 5.98047E1          | 1.8015E1           |     |     | [kg/kmol]       |\n",
            "| Z factor           |            | 3.3876E0           | 8.82547E-1         |     |     | [-]             |\n",
            "| Heat Capacity (Cp) |            | 2.2327E0           | 4.68954E0          |     |     | [kJ/kg*K]       |\n",
            "| Heat Capacity (Cv) |            | 1.95021E0          | 3.50239E0          |     |     | [kJ/kg*K]       |\n",
            "| Speed of Sound     |            | 1.40442E3          | 3.70951E3          |     |     | [m/sec]         |\n",
            "| Enthalpy           | -2.04413E2 | -1.61022E2         | -2.40709E3         |     |     | [kJ/kg]         |\n",
            "| Entropy            | -1.16847E0 | -1.0584E0          | -6.75611E0         |     |     | [kJ/kg*K]       |\n",
            "| JT coefficient     |            | -5.27406E-2        | -2.27317E-2        |     |     | [K/bar]         |\n",
            "|                    |            |                    |                    |     |     |                 |\n",
            "| Viscosity          |            | 2.81482E-4         | 7.48981E-4         |     |     | [kg/m*sec]      |\n",
            "| Conductivity       |            | 1.34033E-1         | 6.31173E-1         |     |     | [W/m*K]         |\n",
            "| SurfaceTension     |            | 2.44637E-2         | 2.44637E-2         |     |     | [N/m]           |\n",
            "|                    |            |                    |                    |     |     |                 |\n",
            "|                    |            |                    |                    |     |     |                 |\n",
            "|                    |            |                    |                    |     |     |                 |\n",
            "| Pressure           |            | 960.0              | 960.0              |     |     | [bar]           |\n",
            "| Temperature        |            | 309.87001699958535 | 309.87001699958535 |     |     | [K]             |\n",
            "|                    |            |                    |                    |     |     |                 |\n",
            "| Model              |            | SRK-EOS            | SRK-EOS            |     |     | -               |\n",
            "| Mixing Rule        |            | classic            | classic            |     |     | -               |\n",
            "|                    |            |                    |                    |     |     |                 |\n",
            "| Stream             |            |                    |                    |     |     | -               |\n",
            "|                    |            |                    |                    |     |     |                 |\n",
            "|                    |            |                    |                    |     |     |                 |\n",
            "|                    |            |                    |                    |     |     |                 |\n",
            "|                    |            |                    |                    |     |     |                 |\n",
            "Composition of aqueous phase:\n",
            "| 0         | 1              | 2            | 3          |\n",
            "|:----------|:---------------|:-------------|:-----------|\n",
            "| Component | mmol/kgSolvent | mg/kgSolvent | Act.Coef   |\n",
            "| nitrogen  | 0.0073912      | 0.207053     | 9.43226E-7 |\n",
            "| CO2       | 0.341905       | 15.0472      | 1.02448    |\n",
            "| methane   | 1.52833        | 24.5191      | 1.02183    |\n",
            "| ethane    | 0.129297       | 3.88795      | 1.02922    |\n",
            "| propane   | 0.0114208      | 0.503625     | 1.03811    |\n",
            "| water     | 55509.3        | 1000000.0    | 1.00003    |\n",
            "| Na+       | 20.957         | 481.801      | 0.864912   |\n",
            "| Cl-       | 20.957         | 742.926      | 0.874832   |\n",
            "| Ca++      | 0.20957        | 8.39956      | 0.561159   |\n",
            "| Fe++      | 0.20957        | 11.7045      | 0.560018   |\n",
            "| OH-       | 1.76143E-5     | 2.99461E-4   | 0.874291   |\n",
            "| H3O+      | 0.0260958      | 0.496342     | 0.861623   |\n",
            "| HCO3-     | 0.233942       | 14.2705      | 0.871737   |\n",
            "| CO3--     | 8.5313E-4      | 0.0511725    | 0.557349   |\n",
            "|           |                |              |            |\n",
            "Relative solubility in salts:\n"
          ]
        }
      ],
      "source": [
        "fluidCorr = template1.getOutStream().getFluid().clone()\n",
        "\n",
        "from neqsim.thermo import fluid, TPflash, checkScalePotential\n",
        "nitrogen = 1.0 #@param {type:\"number\"}\n",
        "CO2 = 1.1 #@param {type:\"number\"}\n",
        "methane = 88.3  #@param {type:\"number\"}\n",
        "ethane = 5.0  #@param {type:\"number\"}\n",
        "propane =  1.5 #@param {type:\"number\"}\n",
        "water =  1.25 #@param {type:\"number\"}\n",
        "Naplus =  0.025 #@param {type:\"number\"}\n",
        "Clminus =  0.025 #@param {type:\"number\"}\n",
        "Caplus =  0.00025 #@param {type:\"number\"}\n",
        "Feplus =  0.00025 #@param {type:\"number\"}\n",
        "OHminus =  0.00025 #@param {type:\"number\"}\n",
        "\n",
        "formationWater = fluid('Electrolyte-CPA-EoS')\n",
        "formationWater.addComponent(\"nitrogen\", nitrogen)\n",
        "formationWater.addComponent(\"CO2\", CO2)\n",
        "formationWater.addComponent(\"methane\", methane)\n",
        "formationWater.addComponent(\"ethane\", ethane)\n",
        "formationWater.addComponent(\"propane\", propane)\n",
        "formationWater.addComponent(\"water\", water,\"kg/sec\")\n",
        "formationWater.addComponent(\"Na+\", Naplus,\"mol/sec\")\n",
        "formationWater.addComponent(\"Cl-\", Clminus,\"mol/sec\")\n",
        "formationWater.addComponent(\"Ca++\", Caplus,\"mol/sec\")\n",
        "formationWater.addComponent(\"Fe++\", Feplus,\"mol/sec\")\n",
        "formationWater.addComponent(\"OH-\", OHminus,\"mol/sec\")\n",
        "formationWater.chemicalReactionInit()\n",
        "formationWater.setMixingRule(10)\n",
        "\n",
        "TPflash(formationWater)\n",
        "ionCompResults = calcIonComposition(formationWater)\n",
        "scaleResults = checkScalePotential(formationWater)\n",
        "\n",
        "print(\"pH of water \",formationWater.getPhase(\"aqueous\").getpH())\n",
        "\n",
        "import pandas\n",
        "printFrame(fluid1)\n",
        "print(\"Composition of aqueous phase:\")\n",
        "print(pandas.DataFrame(ionCompResults).to_markdown(index=False))\n",
        "print(\"Relative solubility in salts:\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "StAzp9DEiqcT"
      },
      "source": [
        "### Wax control"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JysD1fH2oefz"
      },
      "source": [
        "too be worked on...."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "45IXVPRQiZyQ"
      },
      "outputs": [],
      "source": [
        "fluidWax = template1.getOutStream().getFluid().clone()\n",
        "fluidWax.removeComponent('water')\n",
        "fluidWax.getWaxModel().addTBPWax();\n",
        "#fluidWax.addSolidComplexPhase(\"wax\");\n",
        "fluidWax.setMultiphaseWaxCheck(True);\n",
        "fluidWax.setTemperature(20.112, \"C\")\n",
        "fluidWax.setPressure(10.0, \"bara\")\n",
        "\n",
        "#TPflash(fluidWax)\n",
        "#printFrame(fluidWax)\n",
        "#waxTemp = WAT(fluidWax)-273.15\n",
        "#printFrame(fluid1)\n",
        "#print(\"WAT \", waxTemp, \" °C\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aCq6NpNDZQl_"
      },
      "source": [
        "#7. Offshore process (FPSO)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VFseuza94KOE"
      },
      "source": [
        "## Process design criteria"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GLU2D2gk4PMP"
      },
      "source": [
        ".... process design specifications"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NlhLj_s7GeGR"
      },
      "source": [
        "## Process simulation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "McRkZQBpLEeG"
      },
      "source": [
        "......."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "kNfuxLcpExGO"
      },
      "outputs": [],
      "source": [
        "inputdata = {\n",
        "  'topsidePressure': 90.0,\n",
        "  'topsideTemperature': 46.16,\n",
        "  'temperatureOilHeater' : 60.0,\n",
        "  'temperatureOilHeater2' : 91.0,\n",
        "  'secondStagePressure': 22.4,\n",
        "  'thirdStagePressure': 5.7,\n",
        "  'fourthStagePressure': 1.96,\n",
        "  'firstStageSuctionCoolerTemperature': 45.0,\n",
        "  'secondStageSuctionCoolerTemperature': 40.0,\n",
        "  'thirdStageSuctionCoolerTemperature':designTemperatureProcessAfterSeaWaterCooler,\n",
        "  'dewPointSuctionCoolerTemperature':designTemperatureProcessAfterSeaWaterCooler,\n",
        "  'firstStageExportCoolerTemperature': designTemperatureProcessAfterSeaWaterCooler,\n",
        "  'secondStageExportCoolerTemperature': designTemperatureProcessAfterSeaWaterCooler,\n",
        "  'thirdStageExportCoolerTemperature': designTemperatureProcessAfterSeaWaterCooler,\n",
        "  'firststageexportpressure': 250.0,\n",
        "  'secondstageexportpressure': 474.0,\n",
        "  'thirdstageexportpressure': 633.0,\n",
        "}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ei7FBCFY3mVo",
        "outputId": "dad59d6d-9bfb-4303-e655-43217112c7c7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0]\n"
          ]
        }
      ],
      "source": [
        "from neqsim.thermo import fluid, printFrame\n",
        "from neqsim.process import compsplitter, compressor, splitter, cooler, separator3phase, getProcess, clearProcess, mixer, heater, stream, pump, separator, runProcess, stream, saturator, valve, filters, heatExchanger, simpleTEGAbsorber,distillationColumn, waterStripperColumn, recycle2, setpoint, calculator\n",
        "\n",
        "clearProcess()\n",
        "\n",
        "wellFluid = stream(productionriser.getOutStream().clone())\n",
        "#wellFluid.setFlowRate(1757595.58297715, 'kg/hr')\n",
        "\n",
        "feedTPsetter = heater(wellFluid)\n",
        "feedTPsetter.setName('inletTP')\n",
        "feedTPsetter.setOutPressure(inputdata['topsidePressure'], \"bara\")\n",
        "feedTPsetter.setOutTemperature(inputdata['topsideTemperature'], \"C\")\n",
        "\n",
        "feedToOffshoreProcess = stream(feedTPsetter.getOutStream())\n",
        "feedToOffshoreProcess.setName(\"feed to offshore\")\n",
        "\n",
        "firstStageSeparator = separator3phase(feedToOffshoreProcess)\n",
        "firstStageSeparator.setName(\"1st stage separator\")\n",
        "\n",
        "oilThrotValve = valve(firstStageSeparator.getOilOutStream())\n",
        "oilThrotValve.setName(\"valve oil from first stage\")\n",
        "oilThrotValve.setOutletPressure(inputdata['secondStagePressure'])\n",
        "\n",
        "oilThirdStageToMix2 =  stream(wellFluid.clone())\n",
        "oilThirdStageToMix2.setName(\"resyc oil to mixer 2\")\n",
        "\n",
        "HPMixer = mixer(\"HP oil mixer\")\n",
        "HPMixer.addStream(oilThrotValve.getOutStream())\n",
        "HPMixer.addStream(oilThirdStageToMix2)\n",
        "\n",
        "oilHeaterFromFirstStage = heater(HPMixer.getOutStream())\n",
        "oilHeaterFromFirstStage.setName(\"oil heater second stage\")\n",
        "oilHeaterFromFirstStage.setOutTemperature(inputdata['temperatureOilHeater'],'C')\n",
        "\n",
        "secondStageSeparator = separator3phase(oilHeaterFromFirstStage.getOutStream())\n",
        "secondStageSeparator.addStream(oilThirdStageToMix2)\n",
        "secondStageSeparator.setName(\"2nd stage separator\")\n",
        "\n",
        "oilHeaterFromSecondStage = heater(secondStageSeparator.getOilOutStream())\n",
        "oilHeaterFromSecondStage.setName(\"oil heater third stage\")\n",
        "oilHeaterFromSecondStage.setOutTemperature(inputdata['temperatureOilHeater2'],'C')\n",
        "\n",
        "oilThrotValve2 = valve(oilHeaterFromSecondStage.getOutStream())\n",
        "oilThrotValve2.setName(\"valve oil from second stage\")\n",
        "oilThrotValve2.setOutletPressure(inputdata['thirdStagePressure'])\n",
        "\n",
        "thirdStageSeparator = separator3phase(oilThrotValve2.getOutStream())\n",
        "thirdStageSeparator.setName(\"3rd stage separator\")\n",
        "\n",
        "oilThirdStageToSep =  stream(wellFluid.clone())\n",
        "oilThirdStageToSep.setName(\"resyc oil\")\n",
        "thirdStageSeparator.addStream(oilThirdStageToSep)\n",
        "\n",
        "oilThrotValve3 = valve(thirdStageSeparator.getOilOutStream())\n",
        "oilThrotValve3.setName(\"valve oil from third stage\")\n",
        "oilThrotValve3.setOutletPressure(inputdata['fourthStagePressure'])\n",
        "\n",
        "fourthStageSeparator = separator3phase(oilThrotValve3.getOutStream())\n",
        "fourthStageSeparator.setName(\"4th stage separator\")\n",
        "\n",
        "stableOil = stream(fourthStageSeparator.getOilOutStream())\n",
        "stableOil.setName(\"stable oil\")\n",
        "\n",
        "stableOilPump = pump(stableOil,15.0,\"stable oil pump\")\n",
        "\n",
        "firstStageCooler = cooler(fourthStageSeparator.getGasOutStream())\n",
        "firstStageCooler.setName(\"1st stage cooler\")\n",
        "firstStageCooler.setOutTemperature(inputdata['firstStageSuctionCoolerTemperature'],'C')\n",
        "\n",
        "firstStageScrubber = separator(firstStageCooler.getOutStream())\n",
        "firstStageScrubber.setName(\"1st stage scrubber\")\n",
        "\n",
        "firstStageCompressor = compressor(firstStageScrubber.getGasOutStream())\n",
        "firstStageCompressor.setName(\"1st stage compressor\")\n",
        "firstStageCompressor.setOutletPressure(inputdata['thirdStagePressure'])\n",
        "firstStageCompressor.setIsentropicEfficiency(0.75)\n",
        "\n",
        "secondstageGasMixer = mixer(\"seccond Stage mixer\")\n",
        "secondstageGasMixer.addStream(firstStageCompressor.getOutStream())\n",
        "secondstageGasMixer.addStream(thirdStageSeparator.getGasOutStream())\n",
        "\n",
        "secondStageCooler = cooler(secondstageGasMixer.getOutStream())\n",
        "secondStageCooler.setName(\"2nd stage cooler\")\n",
        "secondStageCooler.setOutTemperature(inputdata['secondStageSuctionCoolerTemperature'],'C')\n",
        "\n",
        "secondStageScrubber = separator(secondStageCooler.getOutStream())\n",
        "secondStageScrubber.setName(\"2nd stage scrubber\")\n",
        "\n",
        "secondStageCompressor = compressor(secondStageScrubber.getGasOutStream())\n",
        "secondStageCompressor.setName(\"2nd stage compressor\")\n",
        "secondStageCompressor.setOutletPressure(inputdata['secondStagePressure'])\n",
        "secondStageCompressor.setIsentropicEfficiency(0.75)\n",
        "\n",
        "thirdstageGasMixer = mixer(\"third Stage mixer\")\n",
        "thirdstageGasMixer.addStream(secondStageCompressor.getOutStream())\n",
        "thirdstageGasMixer.addStream(secondStageSeparator.getGasOutStream())\n",
        "\n",
        "thirdStageCooler = cooler(thirdstageGasMixer.getOutStream())\n",
        "thirdStageCooler.setName(\"3rd stage cooler\")\n",
        "thirdStageCooler.setOutTemperature(inputdata['thirdStageSuctionCoolerTemperature'],'C')\n",
        "\n",
        "thirdStageScrubber = separator(thirdStageCooler.getOutStream())\n",
        "thirdStageScrubber.setName(\"3rd stage scrubber\")\n",
        "\n",
        "thirdStageCompressor = compressor(thirdStageScrubber.getGasOutStream())\n",
        "thirdStageCompressor.setName(\"3rd stage compressor\")\n",
        "thirdStageCompressor.setOutletPressure(inputdata['topsidePressure'])\n",
        "thirdStageCompressor.setIsentropicEfficiency(0.75)\n",
        "\n",
        "richGasMixer = mixer(\"fourth Stage mixer\")\n",
        "richGasMixer.addStream(thirdStageCompressor.getOutStream())\n",
        "richGasMixer.addStream(firstStageSeparator.getGasOutStream())\n",
        "\n",
        "dewPointControlCooler = cooler(richGasMixer.getOutStream())\n",
        "dewPointControlCooler.setName(\"dew point cooler\")\n",
        "dewPointControlCooler.setOutTemperature(inputdata['dewPointSuctionCoolerTemperature'],'C')\n",
        "\n",
        "dewPointScrubber = separator(dewPointControlCooler.getOutStream())\n",
        "dewPointScrubber.setName(\"dew point scrubber\")\n",
        "\n",
        "lpLiqmixer = mixer(\"LP liq mixer\");\n",
        "lpLiqmixer.addStream(firstStageScrubber.getLiquidOutStream());\n",
        "lpLiqmixer.addStream(secondStageScrubber.getLiquidOutStream());\n",
        "lpLiqmixer.addStream(thirdStageScrubber.getLiquidOutStream());\n",
        "\n",
        "hpResycle = recycle2(\"HP liq resycle\")\n",
        "hpResycle.addStream(dewPointScrubber.getLiquidOutStream())\n",
        "hpResycle.setOutletStream(oilThirdStageToMix2)\n",
        "\n",
        "lpResycle = recycle2(\"LP liq resycle\")\n",
        "lpResycle.addStream(lpLiqmixer.getOutStream())\n",
        "lpResycle.setOutletStream(oilThirdStageToSep)\n",
        "\n",
        "compsplits = [1.0]*dewPointScrubber.getGasOutStream().getFluid().getNumberOfComponents()\n",
        "compsplits[-1] = 0.0\n",
        "print(compsplits)\n",
        "\n",
        "compsplitter =  compsplitter(dewPointScrubber.getGasOutStream(), compsplits, name=\"comp slitter\")\n",
        "\n",
        "dehydratedgas = stream(compsplitter.getSplitStream(0))\n",
        "\n",
        "streamSplit = splitter(dehydratedgas)\n",
        "streamSplit.setFlowRates(JDouble[:]([-1, 0.73]), 'MSm3/day')\n",
        "\n",
        "fuelgas = stream(streamSplit.getSplitStream(1))\n",
        "fuelgas.setName('fuelgas')\n",
        "\n",
        "exportCompressor1 = compressor(streamSplit.getSplitStream(0))\n",
        "exportCompressor1.setName(\"export 1st stage\")\n",
        "exportCompressor1.setOutletPressure(inputdata['firststageexportpressure'])\n",
        "exportCompressor1.setIsentropicEfficiency(0.75)\n",
        "\n",
        "exportCooler = cooler(exportCompressor1.getOutStream())\n",
        "exportCooler.setName(\"export cooler\")\n",
        "exportCooler.setOutTemperature(inputdata['secondStageExportCoolerTemperature'],'C')\n",
        "\n",
        "splitter = splitter(exportCooler.getOutStream())#, [0.5, 0.5])\n",
        "splitter.setFlowRates(JDouble[:]([5.0, -1]), 'MSm3/day')\n",
        "\n",
        "exportGas = stream(splitter.getSplitStream(0))\n",
        "exportGas.setName(\"export gas\")\n",
        "\n",
        "injectionGas = stream(splitter.getSplitStream(1))\n",
        "injectionGas.setName(\"injection gas\")\n",
        "\n",
        "injectionCompressor1= compressor(injectionGas)\n",
        "injectionCompressor1.setName(\"injetion 1st stage\")\n",
        "injectionCompressor1.setOutletPressure(inputdata['secondstageexportpressure'])\n",
        "injectionCompressor1.setIsentropicEfficiency(0.8)\n",
        "\n",
        "injectionCooler = cooler(injectionCompressor1.getOutStream())\n",
        "injectionCooler.setName(\"injection cooler 1\")\n",
        "injectionCooler.setOutTemperature(inputdata['secondStageExportCoolerTemperature'],'C')\n",
        "\n",
        "injectionCompressor2= compressor(injectionCooler.getOutStream())\n",
        "injectionCompressor2.setName(\"injetion 2nd stage\")\n",
        "injectionCompressor2.setOutletPressure(inputdata['thirdstageexportpressure'])\n",
        "injectionCompressor2.setIsentropicEfficiency(0.70)\n",
        "\n",
        "injectionCooler2 = cooler(injectionCompressor2.getOutStream())\n",
        "injectionCooler2.setName(\"injection cooler 2\")\n",
        "injectionCooler2.setOutTemperature(inputdata['thirdStageExportCoolerTemperature'],'C')\n",
        "\n",
        "oilprocess = getProcess()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8_tTbuP55o73",
        "outputId": "140b6481-c35d-478b-cea7-22cd76aa72aa"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "flow rate water to sea  3.071633209823204  m3/hr\n",
            "flow rate water to sea  0.46369374935491087  kbbl/day\n",
            "oil in water  0.014551236104942689  ppm mole\n"
          ]
        }
      ],
      "source": [
        "# Water handeling\n",
        "waterfromFirstStage = firstStageSeparator.getWaterOutStream()\n",
        "waterfromSecondStage = secondStageSeparator.getWaterOutStream()\n",
        "waterfromThirdStage = thirdStageSeparator.getWaterOutStream()\n",
        "\n",
        "watermixer = mixer('water mixer')\n",
        "watermixer.addStream(waterfromFirstStage)\n",
        "#watermixer.addStream(waterfromSecondStage)\n",
        "#watermixer.addStream(waterfromThirdStage)\n",
        "watermixer.run()\n",
        "\n",
        "waterheater = heater(watermixer.getOutletStream())\n",
        "waterheater.setOutTemperature(30.0, 'C')\n",
        "waterheater.setOutPressure(2.0, 'bara')\n",
        "waterheater.run()\n",
        "\n",
        "waterdegaser = separator3phase(waterheater.getOutletStream())\n",
        "waterdegaser.run()\n",
        "\n",
        "waterratetosea =  waterdegaser.getWaterOutStream()\n",
        "\n",
        "print('flow rate water to sea ', waterratetosea.getFlowRate('kg/hr')/1000.0, ' m3/hr')\n",
        "print('flow rate water to sea ', waterratetosea.getFlowRate('kg/hr')/1000.0*6.29*24/1e3, ' kbbl/day')\n",
        "\n",
        "print('oil in water ', (1-0-waterratetosea.getFluid().getPhase(0).getComponent('water').getx())*1e6, ' ppm mole')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "CL--SLYe4GWL"
      },
      "outputs": [],
      "source": [
        "#oilprocess.run() #run this to finish thread before continuing\n",
        "thread = oilprocess.runAsThread()\n",
        "thread.join(50000) # set maximum time to 50000 msec before continuing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ox8QIyd4giQn",
        "outputId": "150aea10-2cfa-4480-940e-e19e9c526532"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "massbalance  99.99663185420923\n"
          ]
        }
      ],
      "source": [
        "# Check mass balance of oil and gas in simulation (should be ~100%)\n",
        "massbalance = (exportGas.getFlowRate('kg/hr') + injectionGas.getFlowRate('kg/hr') + stableOilPump.getOutStream().getFlowRate('kg/hr') + fuelgas.getFlowRate('kg/hr')) / (feedToOffshoreProcess.getFluid().getPhase('gas').getFlowRate('kg/hr') + feedToOffshoreProcess.getFluid().getPhase('oil').getFlowRate('kg/hr'))\n",
        "print('massbalance ' , massbalance*100)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bMsT6QnSc4zg"
      },
      "source": [
        "water injection\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GCBhe5nxc708",
        "outputId": "fa1c13c6-eda4-40f2-ef8b-db5c176c96ee"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "water injection pump power  6076.895431969226  kW\n"
          ]
        }
      ],
      "source": [
        "waterStream = injectionWaterStream.clone()\n",
        "waterStream.setPressure(1.0, 'bara')\n",
        "waterStream.setTemperature(20.0, 'C')\n",
        "waterStream.run()\n",
        "\n",
        "waterpump = pump(waterStream)\n",
        "waterpump.setOutletPressure(500.0)\n",
        "\n",
        "waterpump.run()\n",
        "\n",
        "print('water injection pump power ', waterpump.getPower()/1e3, ' kW')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "waRZD5U9ByL7",
        "outputId": "f7f541c7-dcea-4a8f-ecc1-49fe6b3064c4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "| 0                  | 1          | 2                  | 3                  | 4                  | 5   | 6               |\n",
            "|:-------------------|:-----------|:-------------------|:-------------------|:-------------------|:----|:----------------|\n",
            "|                    | total      | GAS                | OIL                | AQUEOUS            |     |                 |\n",
            "| nitrogen           | 1.03623E-2 | 2.70841E-2         | 3.05732E-3         | 2.713E-8           |     | [mole fraction] |\n",
            "| CO2                | 6.74624E-5 | 1.27729E-4         | 7.88121E-5         | 1.50173E-7         |     | [mole fraction] |\n",
            "| methane            | 3.20627E-1 | 7.40367E-1         | 2.13355E-1         | 1.13715E-6         |     | [mole fraction] |\n",
            "| ethane             | 9.89745E-2 | 1.58642E-1         | 1.50861E-1         | 7.60094E-9         |     | [mole fraction] |\n",
            "| propane            | 5.27234E-2 | 5.49201E-2         | 1.16342E-1         | 3.07913E-12        |     | [mole fraction] |\n",
            "| i-butane           | 7.58758E-3 | 5.55944E-3         | 1.95937E-2         | 1.02215E-15        |     | [mole fraction] |\n",
            "| n-butane           | 1.29059E-2 | 7.58236E-3         | 3.56061E-2         | 2.06634E-15        |     | [mole fraction] |\n",
            "| i-pentane          | 3.92346E-3 | 1.46829E-3         | 1.18419E-2         | 2.57091E-18        |     | [mole fraction] |\n",
            "| n-pentane          | 4.40681E-3 | 1.38868E-3         | 1.36175E-2         | 1.39922E-18        |     | [mole fraction] |\n",
            "| n-hexane           | 4.55586E-3 | 7.50195E-4         | 1.49116E-2         | 1.13193E-21        |     | [mole fraction] |\n",
            "| C7_PC              | 6.37927E-3 | 3.62318E-4         | 2.17165E-2         | 3.28195E-16        |     | [mole fraction] |\n",
            "| C8_PC              | 6.94726E-3 | 2.20516E-4         | 2.38617E-2         | 1.05641E-18        |     | [mole fraction] |\n",
            "| C9_PC              | 4.41525E-3 | 8.00107E-5         | 1.52382E-2         | 2.14095E-21        |     | [mole fraction] |\n",
            "| C10-C11_PC         | 9.38923E-3 | 7.66051E-5         | 3.25184E-2         | 1.20116E-24        |     | [mole fraction] |\n",
            "| C12-C13_PC         | 8.93938E-3 | 2.55114E-5         | 3.10181E-2         | 6.1015E-29         |     | [mole fraction] |\n",
            "| C14-C15_PC         | 8.49424E-3 | 8.02745E-6         | 2.94933E-2         | 6.5379E-33         |     | [mole fraction] |\n",
            "| C16-C18_PC         | 1.18261E-2 | 2.67747E-6         | 4.10723E-2         | 4.66464E-37        |     | [mole fraction] |\n",
            "| C19-C20_PC         | 7.17405E-3 | 3.6566E-7          | 2.49171E-2         | 7.79674E-41        |     | [mole fraction] |\n",
            "| C21-C23_PC         | 9.6115E-3  | 1.05961E-7         | 3.33834E-2         | 9.94475E-44        |     | [mole fraction] |\n",
            "| C24-C29_PC         | 1.50383E-2 | 1.15648E-8         | 5.22324E-2         | 8.62847E-48        |     | [mole fraction] |\n",
            "| C30_PC             | 3.15198E-2 | 2.49519E-12        | 1.09478E-1         | 1.63188E-57        |     | [mole fraction] |\n",
            "| water              | 3.64131E-1 | 1.33362E-3         | 5.80593E-3         | 9.99999E-1         |     | [mole fraction] |\n",
            "|                    |            |                    |                    |                    |     |                 |\n",
            "| Density            |            | 8.78897E1          | 7.93468E2          | 9.83544E2          |     | [kg/m^3]        |\n",
            "| PhaseFraction      |            | 3.50096E-1         | 2.87911E-1         | 3.61993E-1         |     | [mole fraction] |\n",
            "| MolarMass          | 5.80447E1  | 2.09815E1          | 1.53443E2          | 1.8015E1           |     | [kg/kmol]       |\n",
            "| Z factor           |            | 8.14039E-1         | 6.7332E-1          | 8.1874E-2          |     | [-]             |\n",
            "| Heat Capacity (Cp) |            | 2.89526E0          | 2.11797E0          | 4.79107E0          |     | [kJ/kg*K]       |\n",
            "| Heat Capacity (Cv) |            | 1.74605E0          | 1.94296E0          | 3.44495E0          |     | [kJ/kg*K]       |\n",
            "| Speed of Sound     |            | 3.8395E2           | 1.06446E3          | 3.23253E3          |     | [m/sec]         |\n",
            "| Enthalpy           | -4.62573E2 | -1.52596E1         | -2.43084E2         | -2.45333E3         |     | [kJ/kg]         |\n",
            "| Entropy            | -1.2092E0  | -1.37418E0         | -3.95441E-1        | -6.53611E0         |     | [kJ/kg*K]       |\n",
            "| JT coefficient     |            | 4.07806E-1         | -4.69454E-2        | -2.12894E-2        |     | [K/bar]         |\n",
            "|                    |            |                    |                    |                    |     |                 |\n",
            "| Viscosity          |            | 1.47618E-5         | 8.87359E-4         | 5.84497E-4         |     | [kg/m*sec]      |\n",
            "| Conductivity       |            | 4.3239E-2          | 1.2166E-1          | 6.42899E-1         |     | [W/m*K]         |\n",
            "| SurfaceTension     |            | 2.44637E-2         | 2.44637E-2         |                    |     | [N/m]           |\n",
            "|                    |            |                    | 9.93665E0          | 9.93665E0          |     | [N/m]           |\n",
            "|                    |            | 2.77041E0          |                    | 2.77041E0          |     | [N/m]           |\n",
            "|                    |            |                    |                    |                    |     |                 |\n",
            "| Pressure           |            | 90.0               | 90.0               | 90.0               |     | [bar]           |\n",
            "| Temperature        |            | 319.30999999999995 | 319.30999999999995 | 319.30999999999995 |     | [K]             |\n",
            "|                    |            |                    |                    |                    |     |                 |\n",
            "| Model              |            | SRK-EOS            | SRK-EOS            | SRK-EOS            |     | -               |\n",
            "| Mixing Rule        |            | classic            | classic            | classic            |     | -               |\n",
            "|                    |            |                    |                    |                    |     |                 |\n",
            "| Stream             |            |                    |                    |                    |     | -               |\n",
            "|                    |            |                    |                    |                    |     |                 |\n",
            "|                    |            |                    |                    |                    |     |                 |\n",
            "|                    |            |                    |                    |                    |     |                 |\n",
            "|                    |            |                    |                    |                    |     |                 |\n"
          ]
        }
      ],
      "source": [
        "from neqsim.thermo import fluid, printFrame\n",
        "printFrame(feedToOffshoreProcess.getFluid())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J_AWKAkE4L-T",
        "outputId": "f256a8b7-5504-4e3b-84c9-b1a3347931ca"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "feed flow [kg/hr]  1.3308482022115424\n",
            "cricondenbar [bara]  108.93902671146728\n",
            "recomp power 1 [kW]  341.98458526077127\n",
            "recomp power 2 [kW]  1346.4779931588475\n",
            "recomp power 3 [kW]  3549.346015866531\n",
            "exp power 1 [kW]  9940.5317053054\n",
            "injection power 1 [kW]  1098.821040854483\n",
            "injection power 2 [kW]  712.7270970214172\n",
            "fuel gas [MSm3/d]  0.7300000000000006\n",
            "gas export [MSm3/d]  5.0\n",
            "inj gasflow [MSm3/d]  1.0412992127744047\n",
            "oil export [m3/hr]  1147.3627999031582\n",
            "oil export [kbbl/day]  173.2058882733808\n",
            "oil export [10^6*kg/hr]  0.9589949691998992\n",
            "oil density [kg/m3]  835.8253982792905\n"
          ]
        }
      ],
      "source": [
        "#TVP = stableOil.TVP(20.0, 'C')\n",
        "cricondenbar = exportGas.CCB('bara')\n",
        "powerComp1 = oilprocess.getUnit(\"1st stage compressor\").getPower()/1.0e3\n",
        "powerComp2 = oilprocess.getUnit(\"2nd stage compressor\").getPower()/1.0e3\n",
        "powerComp3 = oilprocess.getUnit(\"3rd stage compressor\").getPower()/1.0e3\n",
        "powerExpComp1 = oilprocess.getUnit(\"export 1st stage\").getPower()/1.0e3\n",
        "powerInjComp1 = oilprocess.getUnit(\"injetion 1st stage\").getPower()/1.0e3\n",
        "powerInjComp2 = oilprocess.getUnit(\"injetion 2nd stage\").getPower()/1.0e3\n",
        "\n",
        "\n",
        "flowRate = exportGas.getFlowRate('MSm3/day')\n",
        "fuelGasflowRate = fuelgas.getFlowRate('MSm3/day')\n",
        "injflowRate = oilprocess.getUnit(\"injetion 2nd stage\").getOutStream().getFlowRate('MSm3/day')\n",
        "flowRateOil = stableOilPump.getOutStream().getFlowRate('kg/hr')\n",
        "oildensity = stableOilPump.getOutStream().getFluid().getDensity('kg/m3')\n",
        "oilkbblperday = flowRateOil/oildensity*6.29*24/1e3\n",
        "\n",
        "print('feed flow [kg/hr] ', feedToOffshoreProcess.getFlowRate('kg/hr')/1e6)\n",
        "#print('TVP [bara] ', TVP)\n",
        "print('cricondenbar [bara] ', cricondenbar)\n",
        "print('recomp power 1 [kW] ', powerComp1)\n",
        "print('recomp power 2 [kW] ', powerComp2)\n",
        "print('recomp power 3 [kW] ', powerComp3)\n",
        "print('exp power 1 [kW] ', powerExpComp1)\n",
        "print('injection power 1 [kW] ', powerInjComp1)\n",
        "print('injection power 2 [kW] ', powerInjComp2)\n",
        "print('fuel gas [MSm3/d] ', fuelGasflowRate)\n",
        "print('gas export [MSm3/d] ', flowRate)\n",
        "print('inj gasflow [MSm3/d] ', injflowRate)\n",
        "print('oil export [m3/hr] ', flowRateOil/oildensity)\n",
        "print('oil export [kbbl/day] ', flowRateOil/oildensity*6.29*24/1e3)\n",
        "print('oil export [10^6*kg/hr] ', flowRateOil/1e6)\n",
        "print('oil density [kg/m3] ', oildensity)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1f4NKB1i5f4z",
        "outputId": "dfa394b1-c353-48ef-9df8-a64db620ad3f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "saturation pressure @0C  69.08944702148438  [bara]\n"
          ]
        }
      ],
      "source": [
        "from neqsim import jNeqSim\n",
        "\n",
        "reservoirTemperature = 0.0 # C\n",
        "fluidsat = exportGas.getFluid().clone()\n",
        "fluidsat.removeComponent('water')\n",
        "fluidsat.setTemperature(reservoirTemperature, 'C')\n",
        "satSim = jNeqSim.PVTsimulation.simulation.SaturationPressure(fluidsat)\n",
        "satSim.run()\n",
        "print('saturation pressure @0C ', fluidsat.getPressure(), ' [bara]')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ld6TEyN24Ybr",
        "outputId": "222c8bc6-e411-4a6e-d17a-c8633592990a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "| 0                  | 1           | 2           | 3   | 4   | 5   | 6               |\n",
            "|:-------------------|:------------|:------------|:----|:----|:----|:----------------|\n",
            "|                    | total       | GAS         |     |     |     |                 |\n",
            "| nitrogen           | 7.03161E-3  | 7.03161E-3  |     |     |     | [mole fraction] |\n",
            "| CO2                | 8.35019E-5  | 8.35019E-5  |     |     |     | [mole fraction] |\n",
            "| methane            | 8.13103E-1  | 8.13103E-1  |     |     |     | [mole fraction] |\n",
            "| ethane             | 9.54389E-2  | 9.54389E-2  |     |     |     | [mole fraction] |\n",
            "| propane            | 5.33057E-2  | 5.33057E-2  |     |     |     | [mole fraction] |\n",
            "| i-butane           | 8.46724E-3  | 8.46724E-3  |     |     |     | [mole fraction] |\n",
            "| n-butane           | 1.44218E-2  | 1.44218E-2  |     |     |     | [mole fraction] |\n",
            "| i-pentane          | 3.14474E-3  | 3.14474E-3  |     |     |     | [mole fraction] |\n",
            "| n-pentane          | 2.96287E-3  | 2.96287E-3  |     |     |     | [mole fraction] |\n",
            "| n-hexane           | 1.18744E-3  | 1.18744E-3  |     |     |     | [mole fraction] |\n",
            "| C7_PC              | 4.42821E-4  | 4.42821E-4  |     |     |     | [mole fraction] |\n",
            "| C8_PC              | 2.38264E-4  | 2.38264E-4  |     |     |     | [mole fraction] |\n",
            "| C9_PC              | 7.88846E-5  | 7.88846E-5  |     |     |     | [mole fraction] |\n",
            "| C10-C11_PC         | 6.68642E-5  | 6.68642E-5  |     |     |     | [mole fraction] |\n",
            "| C12-C13_PC         | 1.90954E-5  | 1.90954E-5  |     |     |     | [mole fraction] |\n",
            "| C14-C15_PC         | 5.17297E-6  | 5.17297E-6  |     |     |     | [mole fraction] |\n",
            "| C16-C18_PC         | 1.45476E-6  | 1.45476E-6  |     |     |     | [mole fraction] |\n",
            "| C19-C20_PC         | 1.71294E-7  | 1.71294E-7  |     |     |     | [mole fraction] |\n",
            "| C21-C23_PC         | 4.38681E-8  | 4.38681E-8  |     |     |     | [mole fraction] |\n",
            "| C24-C29_PC         | 4.06551E-9  | 4.06551E-9  |     |     |     | [mole fraction] |\n",
            "| C30_PC             | 6.10835E-13 | 6.10835E-13 |     |     |     | [mole fraction] |\n",
            "| water              | 0E0         | 0E0         |     |     |     | [mole fraction] |\n",
            "|                    |             |             |     |     |     |                 |\n",
            "| Density            |             | 2.40666E2   |     |     |     | [kg/m^3]        |\n",
            "| PhaseFraction      |             | 1E0         |     |     |     | [mole fraction] |\n",
            "| MolarMass          | 2.0434E1    | 2.0434E1    |     |     |     | [kg/kmol]       |\n",
            "| Z factor           |             | 8.39176E-1  |     |     |     | [-]             |\n",
            "| Heat Capacity (Cp) |             | 3.41202E0   |     |     |     | [kJ/kg*K]       |\n",
            "| Heat Capacity (Cv) |             | 1.89946E0   |     |     |     | [kJ/kg*K]       |\n",
            "| Speed of Sound     |             | 5.57948E2   |     |     |     | [m/sec]         |\n",
            "| Enthalpy           | -1.58969E2  | -1.58969E2  |     |     |     | [kJ/kg]         |\n",
            "| Entropy            | -2.25551E0  | -2.25551E0  |     |     |     | [kJ/kg*K]       |\n",
            "| JT coefficient     |             | 7.95259E-2  |     |     |     | [K/bar]         |\n",
            "|                    |             |             |     |     |     |                 |\n",
            "| Viscosity          |             | 2.73001E-5  |     |     |     | [kg/m*sec]      |\n",
            "| Conductivity       |             | 6.9551E-2   |     |     |     | [W/m*K]         |\n",
            "| SurfaceTension     |             |             |     |     |     | [N/m]           |\n",
            "|                    |             |             |     |     |     |                 |\n",
            "|                    |             |             |     |     |     |                 |\n",
            "|                    |             |             |     |     |     |                 |\n",
            "| Pressure           |             | 250.0       |     |     |     | [bar]           |\n",
            "| Temperature        |             | 309.15      |     |     |     | [K]             |\n",
            "|                    |             |             |     |     |     |                 |\n",
            "| Model              |             | SRK-EOS     |     |     |     | -               |\n",
            "| Mixing Rule        |             | classic     |     |     |     | -               |\n",
            "|                    |             |             |     |     |     |                 |\n",
            "| Stream             |             |             |     |     |     | -               |\n",
            "|                    |             |             |     |     |     |                 |\n",
            "|                    |             |             |     |     |     |                 |\n",
            "|                    |             |             |     |     |     |                 |\n",
            "|                    |             |             |     |     |     |                 |\n",
            "| 0                  | 1           | 2                  | 3   | 4   | 5   | 6               |\n",
            "|:-------------------|:------------|:-------------------|:----|:----|:----|:----------------|\n",
            "|                    | total       | OIL                |     |     |     |                 |\n",
            "| nitrogen           | 5.7335E-8   | 5.7335E-8          |     |     |     | [mole fraction] |\n",
            "| CO2                | 4.58851E-7  | 4.58851E-7         |     |     |     | [mole fraction] |\n",
            "| methane            | 3.01979E-4  | 3.01979E-4         |     |     |     | [mole fraction] |\n",
            "| ethane             | 3.04316E-3  | 3.04316E-3         |     |     |     | [mole fraction] |\n",
            "| propane            | 1.89057E-2  | 1.89057E-2         |     |     |     | [mole fraction] |\n",
            "| i-butane           | 1.11322E-2  | 1.11322E-2         |     |     |     | [mole fraction] |\n",
            "| n-butane           | 3.24203E-2  | 3.24203E-2         |     |     |     | [mole fraction] |\n",
            "| i-pentane          | 2.20407E-2  | 2.20407E-2         |     |     |     | [mole fraction] |\n",
            "| n-pentane          | 2.92495E-2  | 2.92495E-2         |     |     |     | [mole fraction] |\n",
            "| n-hexane           | 4.22585E-2  | 4.22585E-2         |     |     |     | [mole fraction] |\n",
            "| C7_PC              | 6.52426E-2  | 6.52426E-2         |     |     |     | [mole fraction] |\n",
            "| C8_PC              | 6.89026E-2  | 6.89026E-2         |     |     |     | [mole fraction] |\n",
            "| C9_PC              | 4.20163E-2  | 4.20163E-2         |     |     |     | [mole fraction] |\n",
            "| C10-C11_PC         | 8.3749E-2   | 8.3749E-2          |     |     |     | [mole fraction] |\n",
            "| C12-C13_PC         | 7.29893E-2  | 7.29893E-2         |     |     |     | [mole fraction] |\n",
            "| C14-C15_PC         | 6.35369E-2  | 6.35369E-2         |     |     |     | [mole fraction] |\n",
            "| C16-C18_PC         | 8.02101E-2  | 8.02101E-2         |     |     |     | [mole fraction] |\n",
            "| C19-C20_PC         | 4.49002E-2  | 4.49002E-2         |     |     |     | [mole fraction] |\n",
            "| C21-C23_PC         | 5.66971E-2  | 5.66971E-2         |     |     |     | [mole fraction] |\n",
            "| C24-C29_PC         | 8.35368E-2  | 8.35368E-2         |     |     |     | [mole fraction] |\n",
            "| C30_PC             | 1.67522E-1  | 1.67522E-1         |     |     |     | [mole fraction] |\n",
            "| water              | 1.13448E-2  | 1.13448E-2         |     |     |     | [mole fraction] |\n",
            "|                    |             |                    |     |     |     |                 |\n",
            "| Density            |             | 8.35516E2          |     |     |     | [kg/m^3]        |\n",
            "| PhaseFraction      |             | 1E0                |     |     |     | [mole fraction] |\n",
            "| MolarMass          | 2.47095E2   | 2.47095E2          |     |     |     | [kg/kmol]       |\n",
            "| Z factor           |             | 1.56074E-1         |     |     |     | [-]             |\n",
            "| Heat Capacity (Cp) |             | 2.20501E0          |     |     |     | [kJ/kg*K]       |\n",
            "| Heat Capacity (Cv) |             | 2.08314E0          |     |     |     | [kJ/kg*K]       |\n",
            "| Speed of Sound     |             | 1.10125E3          |     |     |     | [m/sec]         |\n",
            "| Enthalpy           | -1.75026E2  | -1.75026E2         |     |     |     | [kJ/kg]         |\n",
            "| Entropy            | -7.78451E-2 | -7.78451E-2        |     |     |     | [kJ/kg*K]       |\n",
            "| JT coefficient     |             | -4.56471E-2        |     |     |     | [K/bar]         |\n",
            "|                    |             |                    |     |     |     |                 |\n",
            "| Viscosity          |             | 3.29931E-3         |     |     |     | [kg/m*sec]      |\n",
            "| Conductivity       |             | 1.15971E-1         |     |     |     | [W/m*K]         |\n",
            "| SurfaceTension     |             |                    |     |     |     | [N/m]           |\n",
            "|                    |             |                    |     |     |     |                 |\n",
            "|                    |             |                    |     |     |     |                 |\n",
            "|                    |             |                    |     |     |     |                 |\n",
            "| Pressure           |             | 15.0               |     |     |     | [bar]           |\n",
            "| Temperature        |             | 357.07453643600513 |     |     |     | [K]             |\n",
            "|                    |             |                    |     |     |     |                 |\n",
            "| Model              |             | SRK-EOS            |     |     |     | -               |\n",
            "| Mixing Rule        |             | classic            |     |     |     | -               |\n",
            "|                    |             |                    |     |     |     |                 |\n",
            "| Stream             |             |                    |     |     |     | -               |\n",
            "|                    |             |                    |     |     |     |                 |\n",
            "|                    |             |                    |     |     |     |                 |\n",
            "|                    |             |                    |     |     |     |                 |\n",
            "|                    |             |                    |     |     |     |                 |\n",
            "| 0                  | 1           | 2           | 3   | 4   | 5   | 6               |\n",
            "|:-------------------|:------------|:------------|:----|:----|:----|:----------------|\n",
            "|                    | total       | GAS         |     |     |     |                 |\n",
            "| nitrogen           | 2.02794E-3  | 2.02794E-3  |     |     |     | [mole fraction] |\n",
            "| CO2                | 1.11328E-4  | 1.11328E-4  |     |     |     | [mole fraction] |\n",
            "| methane            | 5.68037E-1  | 5.68037E-1  |     |     |     | [mole fraction] |\n",
            "| ethane             | 1.76686E-1  | 1.76686E-1  |     |     |     | [mole fraction] |\n",
            "| propane            | 1.52804E-1  | 1.52804E-1  |     |     |     | [mole fraction] |\n",
            "| i-butane           | 2.77398E-2  | 2.77398E-2  |     |     |     | [mole fraction] |\n",
            "| n-butane           | 4.93273E-2  | 4.93273E-2  |     |     |     | [mole fraction] |\n",
            "| i-pentane          | 9.96819E-3  | 9.96819E-3  |     |     |     | [mole fraction] |\n",
            "| n-pentane          | 8.91901E-3  | 8.91901E-3  |     |     |     | [mole fraction] |\n",
            "| n-hexane           | 1.969E-3    | 1.969E-3    |     |     |     | [mole fraction] |\n",
            "| C7_PC              | 1.55695E-4  | 1.55695E-4  |     |     |     | [mole fraction] |\n",
            "| C8_PC              | 1.96701E-5  | 1.96701E-5  |     |     |     | [mole fraction] |\n",
            "| C9_PC              | 1.82227E-6  | 1.82227E-6  |     |     |     | [mole fraction] |\n",
            "| C10-C11_PC         | 3.1272E-7   | 3.1272E-7   |     |     |     | [mole fraction] |\n",
            "| C12-C13_PC         | 1.32226E-8  | 1.32226E-8  |     |     |     | [mole fraction] |\n",
            "| C14-C15_PC         | 5.34258E-10 | 5.34258E-10 |     |     |     | [mole fraction] |\n",
            "| C16-C18_PC         | 1.44597E-11 | 1.44597E-11 |     |     |     | [mole fraction] |\n",
            "| C19-C20_PC         | 1.68032E-13 | 1.68032E-13 |     |     |     | [mole fraction] |\n",
            "| C21-C23_PC         | 4.35303E-15 | 4.35303E-15 |     |     |     | [mole fraction] |\n",
            "| C24-C29_PC         | 8.59802E-18 | 8.59802E-18 |     |     |     | [mole fraction] |\n",
            "| C30_PC             | 4.14263E-27 | 4.14263E-27 |     |     |     | [mole fraction] |\n",
            "| water              | 2.23323E-3  | 2.23323E-3  |     |     |     | [mole fraction] |\n",
            "|                    |             |             |     |     |     |                 |\n",
            "| Density            |             | 2.70194E1   |     |     |     | [kg/m^3]        |\n",
            "| PhaseFraction      |             | 1E0         |     |     |     | [mole fraction] |\n",
            "| MolarMass          | 2.72957E1   | 2.72957E1   |     |     |     | [kg/kmol]       |\n",
            "| Z factor           |             | 8.82553E-1  |     |     |     | [-]             |\n",
            "| Heat Capacity (Cp) |             | 2.15962E0   |     |     |     | [kJ/kg*K]       |\n",
            "| Heat Capacity (Cv) |             | 1.63786E0   |     |     |     | [kJ/kg*K]       |\n",
            "| Speed of Sound     |             | 3.0994E2    |     |     |     | [m/sec]         |\n",
            "| Enthalpy           | 2.96948E1   | 2.96948E1   |     |     |     | [kJ/kg]         |\n",
            "| Entropy            | -4.05295E-1 | -4.05295E-1 |     |     |     | [kJ/kg*K]       |\n",
            "| JT coefficient     |             | 8.38306E-1  |     |     |     | [K/bar]         |\n",
            "|                    |             |             |     |     |     |                 |\n",
            "| Viscosity          |             | 1.1169E-5   |     |     |     | [kg/m*sec]      |\n",
            "| Conductivity       |             | 3.0653E-2   |     |     |     | [W/m*K]         |\n",
            "| SurfaceTension     |             |             |     |     |     | [N/m]           |\n",
            "|                    |             |             |     |     |     |                 |\n",
            "|                    |             |             |     |     |     |                 |\n",
            "|                    |             |             |     |     |     |                 |\n",
            "| Pressure           |             | 22.4        |     |     |     | [bar]           |\n",
            "| Temperature        |             | 309.15      |     |     |     | [K]             |\n",
            "|                    |             |             |     |     |     |                 |\n",
            "| Model              |             | SRK-EOS     |     |     |     | -               |\n",
            "| Mixing Rule        |             | classic     |     |     |     | -               |\n",
            "|                    |             |             |     |     |     |                 |\n",
            "| Stream             |             |             |     |     |     | -               |\n",
            "|                    |             |             |     |     |     |                 |\n",
            "|                    |             |             |     |     |     |                 |\n",
            "|                    |             |             |     |     |     |                 |\n",
            "|                    |             |             |     |     |     |                 |\n"
          ]
        }
      ],
      "source": [
        "from neqsim.thermo import fluid, printFrame\n",
        "printFrame(exportGas.getFluid())\n",
        "printFrame(stableOilPump.getOutStream().getFluid())\n",
        "printFrame(thirdStageScrubber.getGasOutStream().getFluid())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "18KBpM9JAO6m"
      },
      "source": [
        "## calculation of ORF factors"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LtXYwoSA-WmU"
      },
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EtGlIyUj-E9A",
        "outputId": "5675c007-ede3-4e14-d00c-fe14de2d0d01"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "ORF.......................\n",
            "ORF  nitrogen   1.7355375698703026e-06\n",
            "ORF  CO2   0.0011681497852984411\n",
            "ORF  methane   7.904347495164078e-05\n",
            "ORF  ethane   0.006741029302419391\n",
            "ORF  propane   0.0701874604725518\n",
            "ORF  i-butane   0.2186231424625023\n",
            "ORF  n-butane   0.3235781982119941\n",
            "ORF  i-pentane   0.5985752787688043\n",
            "ORF  n-pentane   0.6774391277469581\n",
            "ORF  n-hexane   0.8833297365340528\n",
            "ORF  C7_PC   0.9690885578578582\n",
            "ORF  C8_PC   0.9840103230549445\n",
            "ORF  C9_PC   0.9912551213012379\n",
            "ORF  C10-C11_PC   0.9962626844581206\n",
            "ORF  C12-C13_PC   0.998772307931325\n",
            "ORF  C14-C15_PC   0.9996176251943801\n",
            "ORF  C16-C18_PC   0.9999147965536379\n",
            "ORF  C19-C20_PC   0.9999820769470477\n",
            "ORF  C21-C23_PC   0.9999963649566807\n",
            "ORF  C24-C29_PC   0.9999997713571901\n",
            "ORF  C30_PC   0.9999999999829406\n",
            "ORF  water   0.029551503628945854\n",
            "overall oil recovery factor  0.7225971583023082  [kg oil/kg HC feed]\n"
          ]
        }
      ],
      "source": [
        "componentNames = wellFluid.getFluid().getComponentNames()\n",
        "oilRecoveryFactors = []\n",
        "totalHC = 0.0\n",
        "print ('ORF.......................')\n",
        "for name in componentNames:\n",
        "  total = wellFluid.getFluid().getComponent(name).getTotalFlowRate('kg/hr')\n",
        "  ORF = stableOil.getFluid().getComponent(name).getTotalFlowRate('kg/hr')/total\n",
        "  oilRecoveryFactors.append(ORF)\n",
        "  print('ORF ', name, ' ', ORF)\n",
        "  if wellFluid.getFluid().getComponent(name).isHydrocarbon():\n",
        "    totalHC = totalHC + wellFluid.getFluid().getComponent(name).getTotalFlowRate('kg/hr')\n",
        "\n",
        "overallORF = stableOil.getFlowRate('kg/hr')/totalHC\n",
        "\n",
        "print('overall oil recovery factor ', overallORF, ' [kg oil/kg HC feed]')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5FuFDjGdyE7F"
      },
      "source": [
        "## Power generation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ACljRcqhyK4k"
      },
      "source": [
        "Total power requirements"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KP-caCVGyM2E",
        "outputId": "cb3da8c6-7a6c-4757-ceb5-ab1f332b842f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "total power need  52.857016077805106  MW\n",
            "total heat need  -39.30203046388925  MW\n",
            "total cooling need  -70.2806931149434  MW\n",
            "water injection power  6.076908450559199  MW\n",
            "GCV of export gas  48.78391544962622  MJ/Sm3\n",
            "power production efficiency  30.0 %\n",
            "gas needed for power production  0.3120458964825569  MSm3/day\n"
          ]
        }
      ],
      "source": [
        "waterinjectionpumpduty = waterpump.getDuty()/1e6\n",
        "totalPowerNeed = oilprocess.getPower('MW')+waterinjectionpumpduty\n",
        "totalHeatNeed = oilprocess.getHeaterDuty('W')/1e6\n",
        "totalCoolingNeed = oilprocess.getCoolerDuty('W')/1e6\n",
        "print('total power need ', totalPowerNeed, ' MW')\n",
        "print('total heat need ', totalHeatNeed, ' MW')\n",
        "print('total cooling need ', totalCoolingNeed, ' MW')\n",
        "\n",
        "print('water injection power ', waterinjectionpumpduty, ' MW')\n",
        "GCV = exportGas.GCV()/1e6\n",
        "print('GCV of export gas ', GCV, ' MJ/Sm3')\n",
        "powerProductionEfficicency = 0.3\n",
        "print('power production efficiency ', powerProductionEfficicency*100 , '%')\n",
        "gasforpowerproduction = totalPowerNeed/GCV/powerProductionEfficicency*3600/1e6*24\n",
        "\n",
        "print('gas needed for power production ', gasforpowerproduction, ' MSm3/day')\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "StnN3m47F6q5"
      },
      "source": [
        "### Gas Turbine Design"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7DooCW9eGAAA"
      },
      "outputs": [],
      "source": [
        "#to be implemented\n",
        "stoichmetricair = 1.0"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H3ikTLlB35ia"
      },
      "source": [
        "## Oil and Gas quality"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ceyLgJKI3-dh"
      },
      "source": [
        "...check if oil and gas quality is according to design"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yojpbv48Gi2h"
      },
      "source": [
        "## Process equipment design"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YVy_FaLZLAol",
        "outputId": "3d4ee616-5035-4927-90b6-9581cd9f8b93"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "total volume  102.0  m3\n",
            "total weight  inf  kg\n",
            "first stage separator inner diameter  2.2906942315762167  meter\n"
          ]
        }
      ],
      "source": [
        "design = jNeqSim.processSimulation.mechanicalDesign.SystemMechanicalDesign(oilprocess)\n",
        "design.runDesignCalculation()\n",
        "print('total volume ', design.getTotalVolume(), ' m3')\n",
        "print('total weight ', design.getTotalWeight(), ' kg')\n",
        "\n",
        "print('first stage separator inner diameter ', firstStageSeparator.getMechanicalDesign().innerDiameter, ' meter')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IpoRNPqbKvUH"
      },
      "outputs": [],
      "source": [
        "## inlet separator\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OrTcHvCNBEy1"
      },
      "source": [
        "## Emissions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZQXlyTAQBIMN"
      },
      "source": [
        "Calculation of process efficiency and emissions\n",
        "\n",
        "https://www.ssb.no/_attachment/288060/binary/93858?_version=539789\n",
        "\n",
        "https://www.ssb.no/en/forskning/discussion-papers/_attachment/225118?_ts=14de17b6918"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nHfOjCiYBYT3",
        "outputId": "3be7cd6c-30aa-44d2-8d29-230823f2197c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "gas export [MSm3/d]  5.999999999999999\n",
            "oil export [m3/hr]  1488.030730986197\n",
            "production  35712.74354366873 oe/day\n",
            "Co2 efficiency  12.232727360791866  kg CO2/m3 oe\n",
            "Co2 efficiency  1.9448515629736822  kg CO2/fat oe\n"
          ]
        }
      ],
      "source": [
        "print('gas export [MSm3/d] ', flowRate)\n",
        "print('oil export [m3/hr] ', flowRateOil/oildensity)\n",
        "\n",
        "totalproductionoe = flowRate/1e3 + flowRateOil/oildensity*24\n",
        "\n",
        "print('production ', totalproductionoe, 'oe/day')\n",
        "\n",
        "emissionFactor = 2.0 #tonn CO2/ tons NG\n",
        "gasdensity = 0.7 # kg/Sm3\n",
        "CO2emission = gasforpowerproduction*gasdensity*1e6*emissionFactor/totalproductionoe\n",
        "\n",
        "fatperm3 = 6.2898\n",
        "print('Co2 efficiency ', CO2emission, ' kg CO2/m3 oe')\n",
        "print('Co2 efficiency ', CO2emission/fatperm3, ' kg CO2/fat oe')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rnqyxV-tZpFd"
      },
      "source": [
        "#8. Gas export pipeline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IixP4RfL8MGX"
      },
      "outputs": [],
      "source": [
        "import neqsim\n",
        "from neqsim.thermo.thermoTools import *\n",
        "import matplotlib\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import math\n",
        "plt.style.use('classic')\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XYYhknwl8Q8p",
        "outputId": "056c2659-711b-438f-fd2c-0f7b221b94d9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "inlet flow rate  5.999999999999999\n",
            "inlet pressure  250.0\n",
            "outlet pressure  226.0499497959108\n",
            "outlet temperature  4.754364683842425\n",
            "outlet velocity  4.754364683842425\n"
          ]
        }
      ],
      "source": [
        "from neqsim import methods\n",
        "from neqsim.thermo import fluid, TPflash, createfluid2\n",
        "from neqsim.process import pipe, pipeline, clearProcess, stream, runProcess\n",
        "\n",
        "#Creating stream and pipeline\n",
        "clearProcess()\n",
        "stream1 = stream(exportGas.getFluid().clone())\n",
        "#stream1.setFlowRate(7.0, 'MSm3/day') # Uncomment to test alternative flow rates\n",
        "stream1.run()\n",
        "\n",
        "diam = 17*0.025\n",
        "diameter = [diam, diam, diam, diam, diam] #meter\n",
        "roughnes = [5.0e-6, 5.0e-6, 5.0e-6, 5.0e-6, 5.0e-6] #meter\n",
        "position = [0.0, 5000.0, 10000.0, 20000.0, 270000.0] #meter\n",
        "height = [0.0, -2165.0, -2100.0, -900.0, 0.0] #meter\n",
        "outtemperatures =[278.15, 278.15, 278.15, 278.15, 278.15] #Kelvin\n",
        "outHeatU = [25.0, 25.0, 25.0, 25.0, 25.0] #W/m2K\n",
        "wallHeatU = [35.0, 35.0, 35.0, 35.0, 35.0] #W/m2K\n",
        "\n",
        "pipe1 = pipeline(stream1, position, diameter, height, outtemperatures, roughnes,outHeatU,wallHeatU)\n",
        "pipe1.setNumberOfNodesInLeg(10)\n",
        "runProcess()\n",
        "numberofnodes = pipe1.getPipe().getTotalNumberOfNodes()\n",
        "print('inlet flow rate ', stream1.getFlowRate('MSm3/day'))\n",
        "print('inlet pressure ', stream1.getPressure('bara'))\n",
        "print('outlet pressure ', pipe1.getPipe().getNode(numberofnodes-1).getBulkSystem().getPressure('bara'))\n",
        "print('outlet temperature ', pipe1.getPipe().getNode(numberofnodes-1).getBulkSystem().getTemperature('C'))\n",
        "print('outlet velocity ', pipe1.getPipe().getNode(numberofnodes-1).getBulkSystem().getTemperature('C'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2gPrQ1XR8d4M",
        "outputId": "47097a18-60b0-41f4-d33e-3f8ffbaa0a1c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'position [meter]')"
            ]
          },
          "execution_count": 58,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 960x960 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 960x960 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 960x960 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 960x960 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "pres = []\n",
        "temp = []\n",
        "length = []\n",
        "height = []\n",
        "calcdensity = []\n",
        "gasvelocity = []\n",
        "\n",
        "for node in range (0,pipe1.getPipe().getTotalNumberOfNodes()-1):\n",
        "  pres.append(pipe1.getPipe().getNode(node).getBulkSystem().getPressure('bara'))\n",
        "  temp.append(pipe1.getPipe().getNode(node).getBulkSystem().getTemperature('C'))\n",
        "  height.append(pipe1.getPipe().getNode(node).getVerticalPositionOfNode())\n",
        "  length.append(pipe1.getPipe().getNode(node).getDistanceToCenterOfNode())\n",
        "  calcdensity.append(pipe1.getPipe().getNode(node).getBulkSystem().getDensity('kg/m3'))\n",
        "  gasvelocity.append(pipe1.getPipe().getNode(node).getVelocity())\n",
        "\n",
        "plt.rcParams['figure.figsize'] = [12, 12]\n",
        "plt.figure()\n",
        "plt.subplot(5, 1, 1)\n",
        "plt.plot(length, pres, '-')\n",
        "plt.ylabel('Pressure [bara]')\n",
        "plt.xlabel('Length [meter]')\n",
        "\n",
        "plt.subplot(5, 1, 2)\n",
        "plt.plot(length, temp)\n",
        "plt.xlabel('Length [meter]')\n",
        "plt.ylabel('Temperature[C]')\n",
        "\n",
        "plt.figure()\n",
        "plt.subplot(5, 1, 3)\n",
        "plt.plot(length, calcdensity, '-')\n",
        "plt.ylabel('Density [kg/m3]')\n",
        "plt.xlabel('Length [meter]')\n",
        "\n",
        "plt.figure()\n",
        "plt.subplot(5, 1, 4)\n",
        "plt.plot(length, gasvelocity, '-')\n",
        "plt.ylabel('gasvelocity [m/sec]')\n",
        "plt.xlabel('Length [meter]')\n",
        "\n",
        "plt.figure()\n",
        "plt.subplot(5, 1, 5)\n",
        "plt.plot(length, height, '-')\n",
        "plt.ylabel('height [meter]')\n",
        "plt.xlabel('position [meter]')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PBI9dCQoaHAf"
      },
      "source": [
        "#9. Onshore process"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fvY3C8cd4bLw"
      },
      "source": [
        "## Process design specifications"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EEe8UoY74e5Q"
      },
      "source": [
        "...process design specifications"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WB_sJC1d8O_n"
      },
      "outputs": [],
      "source": [
        "#contractGas = BaseContract(exportgas, \"central\", \"Brazil\")\n",
        "#contractGas.runCheck();\n",
        "#printtable(contractGas.getResultTable())\n",
        "\n",
        "#contractLPG = BaseContract(lpgstream, \"centralLPG\", \"Brazil\")\n",
        "#contractLPG.runCheck();\n",
        "#printtable(contractLPG.getResultTable())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ll2N08PmaUPj"
      },
      "source": [
        "## onshore process simulations"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LnyfqWPpajm7"
      },
      "outputs": [],
      "source": [
        "#NGL expander code\n",
        "from neqsim.process import expander, heater, compsplitter, separator, heatExchanger\n",
        "\n",
        "pressureNGL = 50.6\n",
        "onshoreplant_inlet_pressure = 110.0\n",
        "onshoreplant_inlet_temperature = 20.0\n",
        "\n",
        "clearProcess()\n",
        "\n",
        "inletheater = heater(pipe1.getOutStream())\n",
        "inletheater.setOutPressure(onshoreplant_inlet_pressure, 'bara')\n",
        "inletheater.setOutTemperature(onshoreplant_inlet_temperature, 'C')\n",
        "\n",
        "heatEx = heatExchanger(inletheater.getOutStream())\n",
        "heatEx.setName(\"cross heat-exchanger\")\n",
        "heatEx.setGuessOutTemperature(273.15 - 30.0)\n",
        "heatEx.setUAvalue(3000e3) #assumes an UA vlue of 3000 MW/m2K\n",
        "\n",
        "expanderKarsto = expander(heatEx.getOutStream(0), pressureNGL)\n",
        "expanderKarsto.setOutletPressure(pressureNGL)\n",
        "expanderKarsto.setIsentropicEfficiency(0.75) # assume same efficiency as for compressors\n",
        "\n",
        "scrubberNGL = separator(expanderKarsto.getOutletStream())\n",
        "\n",
        "recyOut = stream(scrubberNGL.getGasOutStream().clone())\n",
        "\n",
        "gasResycle = recycle2(\"gas resycle\")\n",
        "gasResycle.addStream(scrubberNGL.getGasOutStream())\n",
        "gasResycle.setOutletStream(recyOut)\n",
        "\n",
        "heatEx.setFeedStream(1, recyOut)\n",
        "\n",
        "gasfromdeethanizer = stream(heatEx.getOutStream(1).clone())\n",
        "\n",
        "gasmixer = mixer('gas mixer')\n",
        "gasmixer.addStream(heatEx.getOutStream(1))\n",
        "gasmixer.addStream(gasfromdeethanizer)\n",
        "\n",
        "dryGasExportCompressor = compressor(gasmixer.getOutletStream())\n",
        "dryGasExportCompressor.setOutletPressure(100.0, 'bara')\n",
        "dryGasExportCompressor.setIsentropicEfficiency(0.75)\n",
        "\n",
        "exportDryGas = stream(dryGasExportCompressor.getOutStream())\n",
        "\n",
        "NGLvalve1 = valve(scrubberNGL.getLiquidOutStream())\n",
        "NGLvalve1.setOutletPressure(30.0)\n",
        "\n",
        "#FRACTIONATION PROCESS\n",
        "deethanizer = distillationColumn(trays=5, reboil=True, condenser=False, name=\"deethanizer\")\n",
        "deethanizer.addFeedStream(NGLvalve1.getOutStream(),5)\n",
        "deethanizer.getReboiler().setOutTemperature(273.15+105.0)\n",
        "deethanizer.setTopPressure(30.0)\n",
        "deethanizer.setBottomPressure(32.0)\n",
        "\n",
        "compressorgasdeethanizer = compressor(deethanizer.getGasOutStream())\n",
        "compressorgasdeethanizer.setOutletPressure(pressureNGL, 'bara')\n",
        "\n",
        "nglGasResycle = recycle2(\"gas resycle 2\")\n",
        "nglGasResycle.addStream(compressorgasdeethanizer.getOutStream())\n",
        "nglGasResycle.setOutletStream(gasfromdeethanizer)\n",
        "\n",
        "valvetodebutanizer = valve(deethanizer.getLiquidOutStream())\n",
        "valvetodebutanizer.setOutletPressure(14.0)\n",
        "\n",
        "debutanizer = distillationColumn(trays=10, reboil=True, condenser=True, name=\"debutanizer\")\n",
        "debutanizer.addFeedStream(valvetodebutanizer.getOutStream(),9)\n",
        "debutanizer.getCondenser().setRefluxRatio(0.1)\n",
        "debutanizer.getCondenser().setTotalCondenser(True)\n",
        "debutanizer.getReboiler().setOutTemperature(273.15+163.0);\n",
        "debutanizer.setTopPressure(12.8)\n",
        "debutanizer.setBottomPressure(15.0)\n",
        "\n",
        "onshoreprocess = getProcess()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8iGmLVT6jd3l"
      },
      "outputs": [],
      "source": [
        "thread = onshoreprocess.runAsThread()\n",
        "thread.join(60 * 60 * 1000)   # 60 min maks tid"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yLCpv7JmMr86",
        "outputId": "57139ab5-aa52-4d76-cbb5-dc7e0365237f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "mass lost %  2.86400727010578\n"
          ]
        }
      ],
      "source": [
        "#mass balance onshore process should be close to 0% lost\n",
        "massbalanceonshore = (pipe1.getOutStream().getFlowRate('kg/hr') - exportDryGas.getFlowRate('kg/hr') - debutanizer.getLiquidOutStream().getFlowRate('kg/hr') - debutanizer.getGasOutStream().getFlowRate('kg/hr') )/pipe1.getOutStream().getFlowRate('kg/hr')\n",
        "\n",
        "print('mass lost % ',massbalanceonshore*100)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YuS6lu2K6y-u",
        "outputId": "ea9c092a-7505-478f-e1dd-319e544478e4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "feed gas\n",
            "| 0                  | 1           | 2           | 3   | 4   | 5   | 6               |\n",
            "|:-------------------|:------------|:------------|:----|:----|:----|:----------------|\n",
            "|                    | total       | GAS         |     |     |     |                 |\n",
            "| nitrogen           | 7.03161E-3  | 7.03161E-3  |     |     |     | [mole fraction] |\n",
            "| CO2                | 8.35019E-5  | 8.35019E-5  |     |     |     | [mole fraction] |\n",
            "| methane            | 8.13103E-1  | 8.13103E-1  |     |     |     | [mole fraction] |\n",
            "| ethane             | 9.54389E-2  | 9.54389E-2  |     |     |     | [mole fraction] |\n",
            "| propane            | 5.33057E-2  | 5.33057E-2  |     |     |     | [mole fraction] |\n",
            "| i-butane           | 8.46724E-3  | 8.46724E-3  |     |     |     | [mole fraction] |\n",
            "| n-butane           | 1.44218E-2  | 1.44218E-2  |     |     |     | [mole fraction] |\n",
            "| i-pentane          | 3.14474E-3  | 3.14474E-3  |     |     |     | [mole fraction] |\n",
            "| n-pentane          | 2.96287E-3  | 2.96287E-3  |     |     |     | [mole fraction] |\n",
            "| n-hexane           | 1.18744E-3  | 1.18744E-3  |     |     |     | [mole fraction] |\n",
            "| C7_PC              | 4.42821E-4  | 4.42821E-4  |     |     |     | [mole fraction] |\n",
            "| C8_PC              | 2.38264E-4  | 2.38264E-4  |     |     |     | [mole fraction] |\n",
            "| C9_PC              | 7.88846E-5  | 7.88846E-5  |     |     |     | [mole fraction] |\n",
            "| C10-C11_PC         | 6.68642E-5  | 6.68642E-5  |     |     |     | [mole fraction] |\n",
            "| C12-C13_PC         | 1.90954E-5  | 1.90954E-5  |     |     |     | [mole fraction] |\n",
            "| C14-C15_PC         | 5.17297E-6  | 5.17297E-6  |     |     |     | [mole fraction] |\n",
            "| C16-C18_PC         | 1.45476E-6  | 1.45476E-6  |     |     |     | [mole fraction] |\n",
            "| C19-C20_PC         | 1.71294E-7  | 1.71294E-7  |     |     |     | [mole fraction] |\n",
            "| C21-C23_PC         | 4.38681E-8  | 4.38681E-8  |     |     |     | [mole fraction] |\n",
            "| C24-C29_PC         | 4.06551E-9  | 4.06551E-9  |     |     |     | [mole fraction] |\n",
            "| C30_PC             | 6.10835E-13 | 6.10835E-13 |     |     |     | [mole fraction] |\n",
            "| water              | 0E0         | 0E0         |     |     |     | [mole fraction] |\n",
            "|                    |             |             |     |     |     |                 |\n",
            "| Density            |             | 1.31729E2   |     |     |     | [kg/m^3]        |\n",
            "| PhaseFraction      |             | 1E0         |     |     |     | [mole fraction] |\n",
            "| MolarMass          | 2.0434E1    | 2.0434E1    |     |     |     | [kg/kmol]       |\n",
            "| Z factor           |             | 7.06273E-1  |     |     |     | [-]             |\n",
            "| Heat Capacity (Cp) |             | 3.65717E0   |     |     |     | [kJ/kg*K]       |\n",
            "| Heat Capacity (Cv) |             | 1.75663E0   |     |     |     | [kJ/kg*K]       |\n",
            "| Speed of Sound     |             | 3.86043E2   |     |     |     | [m/sec]         |\n",
            "| Enthalpy           | -1.22711E2  | -1.22711E2  |     |     |     | [kJ/kg]         |\n",
            "| Entropy            | -1.8853E0   | -1.8853E0   |     |     |     | [kJ/kg*K]       |\n",
            "| JT coefficient     |             | 3.74821E-1  |     |     |     | [K/bar]         |\n",
            "|                    |             |             |     |     |     |                 |\n",
            "| Viscosity          |             | 1.63765E-5  |     |     |     | [kg/m*sec]      |\n",
            "| Conductivity       |             | 4.67532E-2  |     |     |     | [W/m*K]         |\n",
            "| SurfaceTension     |             |             |     |     |     | [N/m]           |\n",
            "|                    |             |             |     |     |     |                 |\n",
            "|                    |             |             |     |     |     |                 |\n",
            "|                    |             |             |     |     |     |                 |\n",
            "| Pressure           |             | 110.0       |     |     |     | [bar]           |\n",
            "| Temperature        |             | 293.15      |     |     |     | [K]             |\n",
            "|                    |             |             |     |     |     |                 |\n",
            "| Model              |             | SRK-EOS     |     |     |     | -               |\n",
            "| Mixing Rule        |             | classic     |     |     |     | -               |\n",
            "|                    |             |             |     |     |     |                 |\n",
            "| Stream             |             |             |     |     |     | -               |\n",
            "|                    |             |             |     |     |     |                 |\n",
            "|                    |             |             |     |     |     |                 |\n",
            "|                    |             |             |     |     |     |                 |\n",
            "|                    |             |             |     |     |     |                 |\n"
          ]
        }
      ],
      "source": [
        "print('feed gas')\n",
        "printFrame(inletheater.getOutStream().getFluid())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hEkRIUIBlF-z",
        "outputId": "a4d7d2dd-6885-4eae-921f-7206738bb057"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "propane in dry export gas  2.8434904876617453  mol%\n",
            "LPG production rate  841.8028390252096  Sm3/day\n",
            "C5+ rate  149.36636517454994  Sm3/day\n",
            "temperature in NGL scrubber  -40.968212906710306\n",
            "temperature LPG extraction reflux condenser  41.63514678510512\n",
            "GCV  44.671999517153544  MJ/m3\n",
            "duty deethanizer reboiler  7.1684859825422595  MW\n",
            "duty debutanizer reboiler  1.0998551496363158  MW\n",
            "duty debutanizer condenser  -2.2855354272230026  MW\n",
            "propane recovery  0.4966822954087431\n",
            "i-butane recovery  0.7747398445374913\n",
            "n-butane recovery  0.8407670482637131\n"
          ]
        }
      ],
      "source": [
        "print('propane in dry export gas ', exportDryGas.getFluid().getComponent('propane').getx()*100.0, ' mol%')\n",
        "print('LPG production rate ', debutanizer.getGasOutStream().getFlowRate('idSm3/hr')*24.0 , ' Sm3/day')\n",
        "print('C5+ rate ', debutanizer.getLiquidOutStream().getFlowRate('idSm3/hr')*24.0, ' Sm3/day')\n",
        "print('temperature in NGL scrubber ', expanderKarsto.getOutStream().getTemperature('C'))\n",
        "print('temperature LPG extraction reflux condenser ', debutanizer.getGasOutStream().getTemperature('C'))\n",
        "print('GCV ', exportDryGas.GCV()/1e6, ' MJ/m3')\n",
        "\n",
        "print('duty deethanizer reboiler ', deethanizer.getReboiler().getDuty()/1e6, ' MW')\n",
        "print('duty debutanizer reboiler ', debutanizer.getReboiler().getDuty()/1e6, ' MW')\n",
        "print('duty debutanizer condenser ', debutanizer.getCondenser().getDuty()/1e6, ' MW')\n",
        "propaneRecovery = 1.0 - exportDryGas.getFluid().getComponent('propane').getNumberOfmoles()/pipe1.getOutStream().getFluid().getComponent('propane').getNumberOfmoles()\n",
        "ibutaneRecovery = 1.0 - exportDryGas.getFluid().getComponent('i-butane').getNumberOfmoles()/pipe1.getOutStream().getFluid().getComponent('i-butane').getNumberOfmoles()\n",
        "nbutaneRecovery = 1.0 - exportDryGas.getFluid().getComponent('n-butane').getNumberOfmoles()/pipe1.getOutStream().getFluid().getComponent('n-butane').getNumberOfmoles()\n",
        "print('propane recovery ' ,propaneRecovery)\n",
        "print('i-butane recovery ' ,ibutaneRecovery)\n",
        "print('n-butane recovery ' ,nbutaneRecovery)\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8-Y-ezre4mdg"
      },
      "source": [
        "## Gas and liquid product quality"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hAcgEEw84qNg"
      },
      "source": [
        "--- onshore gas and LPG/C5+ quality control"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o2WQMJvg7KR9",
        "outputId": "cd6d32bb-7efc-4b76-8338-746b59529ed3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "export gas\n",
            "| 0                  | 1           | 2                  | 3   | 4   | 5   | 6               |\n",
            "|:-------------------|:------------|:-------------------|:----|:----|:----|:----------------|\n",
            "|                    | total       | GAS                |     |     |     |                 |\n",
            "| nitrogen           | 7.45215E-3  | 7.45215E-3         |     |     |     | [mole fraction] |\n",
            "| CO2                | 8.82677E-5  | 8.82677E-5         |     |     |     | [mole fraction] |\n",
            "| methane            | 8.6152E-1   | 8.6152E-1          |     |     |     | [mole fraction] |\n",
            "| ethane             | 9.76432E-2  | 9.76432E-2         |     |     |     | [mole fraction] |\n",
            "| propane            | 2.84349E-2  | 2.84349E-2         |     |     |     | [mole fraction] |\n",
            "| i-butane           | 2.02145E-3  | 2.02145E-3         |     |     |     | [mole fraction] |\n",
            "| n-butane           | 2.43383E-3  | 2.43383E-3         |     |     |     | [mole fraction] |\n",
            "| i-pentane          | 2.25742E-4  | 2.25742E-4         |     |     |     | [mole fraction] |\n",
            "| n-pentane          | 1.59823E-4  | 1.59823E-4         |     |     |     | [mole fraction] |\n",
            "| n-hexane           | 1.92054E-5  | 1.92054E-5         |     |     |     | [mole fraction] |\n",
            "| C7_PC              | 1.29718E-6  | 1.29718E-6         |     |     |     | [mole fraction] |\n",
            "| C8_PC              | 2.36865E-7  | 2.36865E-7         |     |     |     | [mole fraction] |\n",
            "| C9_PC              | 2.76009E-8  | 2.76009E-8         |     |     |     | [mole fraction] |\n",
            "| C10-C11_PC         | 5.28824E-9  | 5.28824E-9         |     |     |     | [mole fraction] |\n",
            "| C12-C13_PC         | 2.24902E-10 | 2.24902E-10        |     |     |     | [mole fraction] |\n",
            "| C14-C15_PC         | 8.78005E-12 | 8.78005E-12        |     |     |     | [mole fraction] |\n",
            "| C16-C18_PC         | 2.23247E-13 | 2.23247E-13        |     |     |     | [mole fraction] |\n",
            "| C19-C20_PC         | 2.40705E-15 | 2.40705E-15        |     |     |     | [mole fraction] |\n",
            "| C21-C23_PC         | 5.7699E-17  | 5.7699E-17         |     |     |     | [mole fraction] |\n",
            "| C24-C29_PC         | 1.00699E-19 | 1.00699E-19        |     |     |     | [mole fraction] |\n",
            "| C30_PC             | 4.0274E-29  | 4.0274E-29         |     |     |     | [mole fraction] |\n",
            "| water              | 0E0         | 0E0                |     |     |     | [mole fraction] |\n",
            "|                    |             |                    |     |     |     |                 |\n",
            "| Density            |             | 6.85478E1          |     |     |     | [kg/m^3]        |\n",
            "| PhaseFraction      |             | 1E0                |     |     |     | [mole fraction] |\n",
            "| MolarMass          | 1.85126E1   | 1.85126E1          |     |     |     | [kg/kmol]       |\n",
            "| Z factor           |             | 9.15603E-1         |     |     |     | [-]             |\n",
            "| Heat Capacity (Cp) |             | 2.78918E0          |     |     |     | [kJ/kg*K]       |\n",
            "| Heat Capacity (Cv) |             | 1.90083E0          |     |     |     | [kJ/kg*K]       |\n",
            "| Speed of Sound     |             | 4.52748E2          |     |     |     | [m/sec]         |\n",
            "| Enthalpy           | 9.70103E1   | 9.70103E1          |     |     |     | [kJ/kg]         |\n",
            "| Entropy            | -1.44074E0  | -1.44074E0         |     |     |     | [kJ/kg*K]       |\n",
            "| JT coefficient     |             | 2.65092E-1         |     |     |     | [K/bar]         |\n",
            "|                    |             |                    |     |     |     |                 |\n",
            "| Viscosity          |             | 1.52256E-5         |     |     |     | [kg/m*sec]      |\n",
            "| Conductivity       |             | 4.92632E-2         |     |     |     | [W/m*K]         |\n",
            "| SurfaceTension     |             |                    |     |     |     | [N/m]           |\n",
            "|                    |             |                    |     |     |     |                 |\n",
            "|                    |             |                    |     |     |     |                 |\n",
            "|                    |             |                    |     |     |     |                 |\n",
            "| Pressure           |             | 100.0              |     |     |     | [bar]           |\n",
            "| Temperature        |             | 356.12481528832035 |     |     |     | [K]             |\n",
            "|                    |             |                    |     |     |     |                 |\n",
            "| Model              |             | SRK-EOS            |     |     |     | -               |\n",
            "| Mixing Rule        |             | classic            |     |     |     | -               |\n",
            "|                    |             |                    |     |     |     |                 |\n",
            "| Stream             |             |                    |     |     |     | -               |\n",
            "|                    |             |                    |     |     |     |                 |\n",
            "|                    |             |                    |     |     |     |                 |\n",
            "|                    |             |                    |     |     |     |                 |\n",
            "|                    |             |                    |     |     |     |                 |\n"
          ]
        }
      ],
      "source": [
        "print('export gas')\n",
        "printFrame(exportDryGas.getFluid())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "po00pydLvvOQ",
        "outputId": "dceb468e-9549-4b0a-8f52-5cc472690696"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "gas from deethanizer\n",
            "| 0                  | 1           | 2                 | 3   | 4   | 5   | 6               |\n",
            "|:-------------------|:------------|:------------------|:----|:----|:----|:----------------|\n",
            "|                    | total       | GAS               |     |     |     |                 |\n",
            "| nitrogen           | 1.97233E-3  | 1.97233E-3        |     |     |     | [mole fraction] |\n",
            "| CO2                | 1.48954E-4  | 1.48954E-4        |     |     |     | [mole fraction] |\n",
            "| methane            | 6.42432E-1  | 6.42432E-1        |     |     |     | [mole fraction] |\n",
            "| ethane             | 2.58045E-1  | 2.58045E-1        |     |     |     | [mole fraction] |\n",
            "| propane            | 8.4901E-2   | 8.4901E-2         |     |     |     | [mole fraction] |\n",
            "| i-butane           | 5.23394E-3  | 5.23394E-3        |     |     |     | [mole fraction] |\n",
            "| n-butane           | 6.24993E-3  | 6.24993E-3        |     |     |     | [mole fraction] |\n",
            "| i-pentane          | 5.61778E-4  | 5.61778E-4        |     |     |     | [mole fraction] |\n",
            "| n-pentane          | 4.01842E-4  | 4.01842E-4        |     |     |     | [mole fraction] |\n",
            "| n-hexane           | 4.86597E-5  | 4.86597E-5        |     |     |     | [mole fraction] |\n",
            "| C7_PC              | 3.78499E-6  | 3.78499E-6        |     |     |     | [mole fraction] |\n",
            "| C8_PC              | 7.1041E-7   | 7.1041E-7         |     |     |     | [mole fraction] |\n",
            "| C9_PC              | 8.51107E-8  | 8.51107E-8        |     |     |     | [mole fraction] |\n",
            "| C10-C11_PC         | 1.69785E-8  | 1.69785E-8        |     |     |     | [mole fraction] |\n",
            "| C12-C13_PC         | 7.61585E-10 | 7.61585E-10       |     |     |     | [mole fraction] |\n",
            "| C14-C15_PC         | 3.13962E-11 | 3.13962E-11       |     |     |     | [mole fraction] |\n",
            "| C16-C18_PC         | 8.52944E-13 | 8.52944E-13       |     |     |     | [mole fraction] |\n",
            "| C19-C20_PC         | 9.79386E-15 | 9.79386E-15       |     |     |     | [mole fraction] |\n",
            "| C21-C23_PC         | 2.48679E-16 | 2.48679E-16       |     |     |     | [mole fraction] |\n",
            "| C24-C29_PC         | 4.71861E-19 | 4.71861E-19       |     |     |     | [mole fraction] |\n",
            "| C30_PC             | 2.2211E-28  | 2.2211E-28        |     |     |     | [mole fraction] |\n",
            "| water              | 0E0         | 0E0               |     |     |     | [mole fraction] |\n",
            "|                    |             |                   |     |     |     |                 |\n",
            "| Density            |             | 3.68516E1         |     |     |     | [kg/m^3]        |\n",
            "| PhaseFraction      |             | 1E0               |     |     |     | [mole fraction] |\n",
            "| MolarMass          | 2.26133E1   | 2.26133E1         |     |     |     | [kg/kmol]       |\n",
            "| Z factor           |             | 8.24922E-1        |     |     |     | [-]             |\n",
            "| Heat Capacity (Cp) |             | 2.34103E0         |     |     |     | [kJ/kg*K]       |\n",
            "| Heat Capacity (Cv) |             | 1.53484E0         |     |     |     | [kJ/kg*K]       |\n",
            "| Speed of Sound     |             | 3.17441E2         |     |     |     | [m/sec]         |\n",
            "| Enthalpy           | -6.4053E1   | -6.4053E1         |     |     |     | [kJ/kg]         |\n",
            "| Entropy            | -1.08248E0  | -1.08248E0        |     |     |     | [kJ/kg*K]       |\n",
            "| JT coefficient     |             | 9.44009E-1        |     |     |     | [K/bar]         |\n",
            "|                    |             |                   |     |     |     |                 |\n",
            "| Viscosity          |             | 1.05958E-5        |     |     |     | [kg/m*sec]      |\n",
            "| Conductivity       |             | 2.94673E-2        |     |     |     | [W/m*K]         |\n",
            "| SurfaceTension     |             |                   |     |     |     | [N/m]           |\n",
            "|                    |             |                   |     |     |     |                 |\n",
            "|                    |             |                   |     |     |     |                 |\n",
            "|                    |             |                   |     |     |     |                 |\n",
            "| Pressure           |             | 30.0              |     |     |     | [bar]           |\n",
            "| Temperature        |             | 269.1272320673576 |     |     |     | [K]             |\n",
            "|                    |             |                   |     |     |     |                 |\n",
            "| Model              |             | SRK-EOS           |     |     |     | -               |\n",
            "| Mixing Rule        |             | classic           |     |     |     | -               |\n",
            "|                    |             |                   |     |     |     |                 |\n",
            "| Stream             |             |                   |     |     |     | -               |\n",
            "|                    |             |                   |     |     |     |                 |\n",
            "|                    |             |                   |     |     |     |                 |\n",
            "|                    |             |                   |     |     |     |                 |\n",
            "|                    |             |                   |     |     |     |                 |\n",
            "liquid from deethanizer\n",
            "| 0                  | 1           | 2           | 3   | 4   | 5   | 6               |\n",
            "|:-------------------|:------------|:------------|:----|:----|:----|:----------------|\n",
            "|                    | total       | GAS         |     |     |     |                 |\n",
            "| nitrogen           | 3.25682E-8  | 3.25682E-8  |     |     |     | [mole fraction] |\n",
            "| CO2                | 1.98622E-6  | 1.98622E-6  |     |     |     | [mole fraction] |\n",
            "| methane            | 5.11304E-4  | 5.11304E-4  |     |     |     | [mole fraction] |\n",
            "| ethane             | 5.42299E-2  | 5.42299E-2  |     |     |     | [mole fraction] |\n",
            "| propane            | 4.71416E-1  | 4.71416E-1  |     |     |     | [mole fraction] |\n",
            "| i-butane           | 1.17467E-1  | 1.17467E-1  |     |     |     | [mole fraction] |\n",
            "| n-butane           | 2.17239E-1  | 2.17239E-1  |     |     |     | [mole fraction] |\n",
            "| i-pentane          | 5.25482E-2  | 5.25482E-2  |     |     |     | [mole fraction] |\n",
            "| n-pentane          | 5.04031E-2  | 5.04031E-2  |     |     |     | [mole fraction] |\n",
            "| n-hexane           | 2.09504E-2  | 2.09504E-2  |     |     |     | [mole fraction] |\n",
            "| C7_PC              | 7.90624E-3  | 7.90624E-3  |     |     |     | [mole fraction] |\n",
            "| C8_PC              | 4.259E-3    | 4.259E-3    |     |     |     | [mole fraction] |\n",
            "| C9_PC              | 1.41012E-3  | 1.41012E-3  |     |     |     | [mole fraction] |\n",
            "| C10-C11_PC         | 1.19477E-3  | 1.19477E-3  |     |     |     | [mole fraction] |\n",
            "| C12-C13_PC         | 3.41043E-4  | 3.41043E-4  |     |     |     | [mole fraction] |\n",
            "| C14-C15_PC         | 9.2361E-5   | 9.2361E-5   |     |     |     | [mole fraction] |\n",
            "| C16-C18_PC         | 2.59692E-5  | 2.59692E-5  |     |     |     | [mole fraction] |\n",
            "| C19-C20_PC         | 3.05755E-6  | 3.05755E-6  |     |     |     | [mole fraction] |\n",
            "| C21-C23_PC         | 7.83009E-7  | 7.83009E-7  |     |     |     | [mole fraction] |\n",
            "| C24-C29_PC         | 7.25647E-8  | 7.25647E-8  |     |     |     | [mole fraction] |\n",
            "| C30_PC             | 1.09026E-11 | 1.09026E-11 |     |     |     | [mole fraction] |\n",
            "| water              | 0E0         | 0E0         |     |     |     | [mole fraction] |\n",
            "|                    |             |             |     |     |     |                 |\n",
            "| Density            |             | 3.62832E2   |     |     |     | [kg/m^3]        |\n",
            "| PhaseFraction      |             | 1E0         |     |     |     | [mole fraction] |\n",
            "| MolarMass          | 5.28089E1   | 5.28089E1   |     |     |     | [kg/kmol]       |\n",
            "| Z factor           |             | 1.55287E-1  |     |     |     | [-]             |\n",
            "| Heat Capacity (Cp) |             | 4.18721E0   |     |     |     | [kJ/kg*K]       |\n",
            "| Heat Capacity (Cv) |             | 2.08068E0   |     |     |     | [kJ/kg*K]       |\n",
            "| Speed of Sound     |             | 3.05937E2   |     |     |     | [m/sec]         |\n",
            "| Enthalpy           | -8.36425E1  | -8.36425E1  |     |     |     | [kJ/kg]         |\n",
            "| Entropy            | -3.60557E-1 | -3.60557E-1 |     |     |     | [kJ/kg*K]       |\n",
            "| JT coefficient     |             | 2.16559E-1  |     |     |     | [K/bar]         |\n",
            "|                    |             |             |     |     |     |                 |\n",
            "| Viscosity          |             | 5.09721E-5  |     |     |     | [kg/m*sec]      |\n",
            "| Conductivity       |             | 6.30942E-2  |     |     |     | [W/m*K]         |\n",
            "| SurfaceTension     |             |             |     |     |     | [N/m]           |\n",
            "|                    |             |             |     |     |     |                 |\n",
            "|                    |             |             |     |     |     |                 |\n",
            "|                    |             |             |     |     |     |                 |\n",
            "| Pressure           |             | 32.0        |     |     |     | [bar]           |\n",
            "| Temperature        |             | 378.15      |     |     |     | [K]             |\n",
            "|                    |             |             |     |     |     |                 |\n",
            "| Model              |             | SRK-EOS     |     |     |     | -               |\n",
            "| Mixing Rule        |             | classic     |     |     |     | -               |\n",
            "|                    |             |             |     |     |     |                 |\n",
            "| Stream             |             |             |     |     |     | -               |\n",
            "|                    |             |             |     |     |     |                 |\n",
            "|                    |             |             |     |     |     |                 |\n",
            "|                    |             |             |     |     |     |                 |\n",
            "|                    |             |             |     |     |     |                 |\n"
          ]
        }
      ],
      "source": [
        "#De-ethanizer results\n",
        "print('gas from deethanizer')\n",
        "printFrame(deethanizer.getGasOutStream().getFluid())\n",
        "print('liquid from deethanizer')\n",
        "printFrame(deethanizer.getLiquidOutStream().getFluid())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mezh9ctFpwKF",
        "outputId": "2aee39fd-fb31-4a8d-ee00-659989db15ba"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "LPG from top debutanizer \n",
            "| 0                  | 1           | 2                 | 3   | 4   | 5   | 6               |\n",
            "|:-------------------|:------------|:------------------|:----|:----|:----|:----------------|\n",
            "|                    | total       | OIL               |     |     |     |                 |\n",
            "| nitrogen           | 4.45833E-8  | 4.45833E-8        |     |     |     | [mole fraction] |\n",
            "| CO2                | 2.69415E-6  | 2.69415E-6        |     |     |     | [mole fraction] |\n",
            "| methane            | 6.97423E-4  | 6.97423E-4        |     |     |     | [mole fraction] |\n",
            "| ethane             | 7.26606E-2  | 7.26606E-2        |     |     |     | [mole fraction] |\n",
            "| propane            | 5.91258E-1  | 5.91258E-1        |     |     |     | [mole fraction] |\n",
            "| i-butane           | 1.13192E-1  | 1.13192E-1        |     |     |     | [mole fraction] |\n",
            "| n-butane           | 1.76506E-1  | 1.76506E-1        |     |     |     | [mole fraction] |\n",
            "| i-pentane          | 2.37305E-2  | 2.37305E-2        |     |     |     | [mole fraction] |\n",
            "| n-pentane          | 1.88338E-2  | 1.88338E-2        |     |     |     | [mole fraction] |\n",
            "| n-hexane           | 2.83522E-3  | 2.83522E-3        |     |     |     | [mole fraction] |\n",
            "| C7_PC              | 2.44536E-4  | 2.44536E-4        |     |     |     | [mole fraction] |\n",
            "| C8_PC              | 3.56675E-5  | 3.56675E-5        |     |     |     | [mole fraction] |\n",
            "| C9_PC              | 3.06953E-6  | 3.06953E-6        |     |     |     | [mole fraction] |\n",
            "| C10-C11_PC         | 3.54685E-7  | 3.54685E-7        |     |     |     | [mole fraction] |\n",
            "| C12-C13_PC         | 7.5612E-9   | 7.5612E-9         |     |     |     | [mole fraction] |\n",
            "| C14-C15_PC         | 1.4482E-10  | 1.4482E-10        |     |     |     | [mole fraction] |\n",
            "| C16-C18_PC         | 1.52365E-12 | 1.52365E-12       |     |     |     | [mole fraction] |\n",
            "| C19-C20_PC         | 6.83525E-15 | 6.83525E-15       |     |     |     | [mole fraction] |\n",
            "| C21-C23_PC         | 6.82479E-17 | 6.82479E-17       |     |     |     | [mole fraction] |\n",
            "| C24-C29_PC         | 2.66029E-20 | 2.66029E-20       |     |     |     | [mole fraction] |\n",
            "| C30_PC             | 5.28472E-32 | 5.28472E-32       |     |     |     | [mole fraction] |\n",
            "| water              | 0E0         | 0E0               |     |     |     | [mole fraction] |\n",
            "|                    |             |                   |     |     |     |                 |\n",
            "| Density            |             | 4.76321E2         |     |     |     | [kg/m^3]        |\n",
            "| PhaseFraction      |             | 1E0               |     |     |     | [mole fraction] |\n",
            "| MolarMass          | 4.84509E1   | 4.84509E1         |     |     |     | [kg/kmol]       |\n",
            "| Z factor           |             | 5.3509E-2         |     |     |     | [-]             |\n",
            "| Heat Capacity (Cp) |             | 2.9251E0          |     |     |     | [kJ/kg*K]       |\n",
            "| Heat Capacity (Cv) |             | 1.87439E0         |     |     |     | [kJ/kg*K]       |\n",
            "| Speed of Sound     |             | 5.16036E2         |     |     |     | [m/sec]         |\n",
            "| Enthalpy           | -2.7894E2   | -2.7894E2         |     |     |     | [kJ/kg]         |\n",
            "| Entropy            | -1.02552E0  | -1.02552E0        |     |     |     | [kJ/kg*K]       |\n",
            "| JT coefficient     |             | 2.9814E-2         |     |     |     | [K/bar]         |\n",
            "|                    |             |                   |     |     |     |                 |\n",
            "| Viscosity          |             | 8.61676E-5        |     |     |     | [kg/m*sec]      |\n",
            "| Conductivity       |             | 8.14295E-2        |     |     |     | [W/m*K]         |\n",
            "| SurfaceTension     |             |                   |     |     |     | [N/m]           |\n",
            "|                    |             |                   |     |     |     |                 |\n",
            "|                    |             |                   |     |     |     |                 |\n",
            "|                    |             |                   |     |     |     |                 |\n",
            "| Pressure           |             | 13.0              |     |     |     | [bar]           |\n",
            "| Temperature        |             | 314.7851467851051 |     |     |     | [K]             |\n",
            "|                    |             |                   |     |     |     |                 |\n",
            "| Model              |             | SRK-EOS           |     |     |     | -               |\n",
            "| Mixing Rule        |             | classic           |     |     |     | -               |\n",
            "|                    |             |                   |     |     |     |                 |\n",
            "| Stream             |             |                   |     |     |     | -               |\n",
            "|                    |             |                   |     |     |     |                 |\n",
            "|                    |             |                   |     |     |     |                 |\n",
            "|                    |             |                   |     |     |     |                 |\n",
            "|                    |             |                   |     |     |     |                 |\n",
            "C5+ from bottom debutanizer \n",
            "| 0                  | 1           | 2           | 3   | 4   | 5   | 6               |\n",
            "|:-------------------|:------------|:------------|:----|:----|:----|:----------------|\n",
            "|                    | total       | OIL         |     |     |     |                 |\n",
            "| nitrogen           | 7.41091E-21 | 7.41091E-21 |     |     |     | [mole fraction] |\n",
            "| CO2                | 4.41261E-14 | 4.41261E-14 |     |     |     | [mole fraction] |\n",
            "| methane            | 1.2839E-13  | 1.2839E-13  |     |     |     | [mole fraction] |\n",
            "| ethane             | 2.76167E-7  | 2.76167E-7  |     |     |     | [mole fraction] |\n",
            "| propane            | 2.07792E-3  | 2.07792E-3  |     |     |     | [mole fraction] |\n",
            "| i-butane           | 2.17411E-2  | 2.17411E-2  |     |     |     | [mole fraction] |\n",
            "| n-butane           | 1.05943E-1  | 1.05943E-1  |     |     |     | [mole fraction] |\n",
            "| i-pentane          | 1.61722E-1  | 1.61722E-1  |     |     |     | [mole fraction] |\n",
            "| n-pentane          | 2.10105E-1  | 2.10105E-1  |     |     |     | [mole fraction] |\n",
            "| n-hexane           | 2.11033E-1  | 2.11033E-1  |     |     |     | [mole fraction] |\n",
            "| C7_PC              | 1.2658E-1   | 1.2658E-1   |     |     |     | [mole fraction] |\n",
            "| C8_PC              | 8.67523E-2  | 8.67523E-2  |     |     |     | [mole fraction] |\n",
            "| C9_PC              | 3.28031E-2  | 3.28031E-2  |     |     |     | [mole fraction] |\n",
            "| C10-C11_PC         | 2.98612E-2  | 2.98612E-2  |     |     |     | [mole fraction] |\n",
            "| C12-C13_PC         | 8.44809E-3  | 8.44809E-3  |     |     |     | [mole fraction] |\n",
            "| C14-C15_PC         | 2.22416E-3  | 2.22416E-3  |     |     |     | [mole fraction] |\n",
            "| C16-C18_PC         | 6.16295E-4  | 6.16295E-4  |     |     |     | [mole fraction] |\n",
            "| C19-C20_PC         | 7.24412E-5  | 7.24412E-5  |     |     |     | [mole fraction] |\n",
            "| C21-C23_PC         | 1.85634E-5  | 1.85634E-5  |     |     |     | [mole fraction] |\n",
            "| C24-C29_PC         | 1.72153E-6  | 1.72153E-6  |     |     |     | [mole fraction] |\n",
            "| C30_PC             | 2.5871E-10  | 2.5871E-10  |     |     |     | [mole fraction] |\n",
            "| water              | 0E0         | 0E0         |     |     |     | [mole fraction] |\n",
            "|                    |             |             |     |     |     |                 |\n",
            "| Density            |             | 4.79843E2   |     |     |     | [kg/m^3]        |\n",
            "| PhaseFraction      |             | 1E0         |     |     |     | [mole fraction] |\n",
            "| MolarMass          | 8.53314E1   | 8.53314E1   |     |     |     | [kg/kmol]       |\n",
            "| Z factor           |             | 7.98258E-2  |     |     |     | [-]             |\n",
            "| Heat Capacity (Cp) |             | 3.21265E0   |     |     |     | [kJ/kg*K]       |\n",
            "| Heat Capacity (Cv) |             | 2.33859E0   |     |     |     | [kJ/kg*K]       |\n",
            "| Speed of Sound     |             | 3.72509E2   |     |     |     | [m/sec]         |\n",
            "| Enthalpy           | 2.11629E1   | 2.11629E1   |     |     |     | [kJ/kg]         |\n",
            "| Entropy            | 1.97451E-1  | 1.97451E-1  |     |     |     | [kJ/kg*K]       |\n",
            "| JT coefficient     |             | 6.63625E-2  |     |     |     | [K/bar]         |\n",
            "|                    |             |             |     |     |     |                 |\n",
            "| Viscosity          |             | 8.86025E-5  |     |     |     | [kg/m*sec]      |\n",
            "| Conductivity       |             | 6.79941E-2  |     |     |     | [W/m*K]         |\n",
            "| SurfaceTension     |             |             |     |     |     | [N/m]           |\n",
            "|                    |             |             |     |     |     |                 |\n",
            "|                    |             |             |     |     |     |                 |\n",
            "|                    |             |             |     |     |     |                 |\n",
            "| Pressure           |             | 15.0        |     |     |     | [bar]           |\n",
            "| Temperature        |             | 436.15      |     |     |     | [K]             |\n",
            "|                    |             |             |     |     |     |                 |\n",
            "| Model              |             | SRK-EOS     |     |     |     | -               |\n",
            "| Mixing Rule        |             | classic     |     |     |     | -               |\n",
            "|                    |             |             |     |     |     |                 |\n",
            "| Stream             |             |             |     |     |     | -               |\n",
            "|                    |             |             |     |     |     |                 |\n",
            "|                    |             |             |     |     |     |                 |\n",
            "|                    |             |             |     |     |     |                 |\n",
            "|                    |             |             |     |     |     |                 |\n"
          ]
        }
      ],
      "source": [
        "#debutanizer results\n",
        "print('LPG from top debutanizer ')\n",
        "printFrame(debutanizer.getGasOutStream().getFluid())\n",
        "print('C5+ from bottom debutanizer ')\n",
        "printFrame(debutanizer.getLiquidOutStream().getFluid())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n6egzZIneDnJ",
        "outputId": "a372af3e-98c7-48e7-a08b-bf8cfc31747a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "GCV  44.671999517153544  MJ/m3\n",
            "TVP of LPG product  11.879476306124367 bara\n",
            "TVP of C5+ product  1.0148269463267556 bara\n"
          ]
        }
      ],
      "source": [
        "print('GCV ', exportDryGas.GCV()/1e6, ' MJ/m3')\n",
        "print('TVP of LPG product ', debutanizer.getGasOutStream().TVP(37.4, 'C'), 'bara')\n",
        "print('TVP of C5+ product ', debutanizer.getLiquidOutStream().TVP(37.4, 'C'), 'bara')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AL210ftLZ9rB"
      },
      "outputs": [],
      "source": [
        "%%script false --no-raise-error\n",
        "contractGas = BaseContract(exportgas, \"central\", \"Brazil\")\n",
        "contractGas.runCheck();\n",
        "printtable(contractGas.getResultTable())\n",
        "\n",
        "contractLPG = BaseContract(lpgstream, \"centralLPG\", \"Brazil\")\n",
        "contractLPG.runCheck();\n",
        "printtable(contractLPG.getResultTable())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rRsfIS6T2pGg"
      },
      "source": [
        "## Onshore process design"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NdICiQTN2wiR"
      },
      "source": [
        "Process design calculations"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "orV6M2goaLHC"
      },
      "source": [
        "## LPG and Nafta recovery factors"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7yo50ecmaT3w",
        "outputId": "14494abf-8ba3-48f1-b744-27b8f7115f39"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "sales gas recovery  0.8548286940369464\n",
            "LPG recovery  0.09681168803528625\n",
            "C5+ recovery  0.019719545226709562\n"
          ]
        }
      ],
      "source": [
        "LPGrecovery = debutanizer.getGasOutStream().getFlowRate('kg/hr')/pipe1.getOutStream().getFlowRate('kg/hr')\n",
        "C5recovery = debutanizer.getLiquidOutStream().getFlowRate('kg/hr')/pipe1.getOutStream().getFlowRate('kg/hr')\n",
        "salesgasrecovery = exportDryGas.getFlowRate('kg/hr')/pipe1.getOutStream().getFlowRate('kg/hr')\n",
        "\n",
        "print('sales gas recovery ', salesgasrecovery)\n",
        "print('LPG recovery ', LPGrecovery)\n",
        "print('C5+ recovery ', C5recovery)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p-eRlERJKoLV"
      },
      "source": [
        "#10. Energy efficiency and emissions\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zSaK_JWmLpQN"
      },
      "source": [
        "##Calculation of emission in field life time"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V0ngDy7FbKyP"
      },
      "source": [
        "# 11. Cost estimation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UIDlBhclbV6N"
      },
      "outputs": [],
      "source": [
        "pythoncode9=23"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oYxUQtNHbmbG"
      },
      "source": [
        "## CAPEX"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HCmLTdX7b2uF"
      },
      "outputs": [],
      "source": [
        "pythoncpde=12"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uYSkwhvvb-C2"
      },
      "source": [
        "## OPEX"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4325x-g6cEaa"
      },
      "outputs": [],
      "source": [
        "code=12"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xyGeu0hALZpJ"
      },
      "source": [
        "## oil gas lpg c5+ production profile"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BxbYBDFILkFZ"
      },
      "source": [
        "......."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HNCfjOZucTfb"
      },
      "source": [
        "# 12. Asset Value estimation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kL83xhJaecfp"
      },
      "source": [
        "## Total production in current year"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jeCQBN3-gi-k",
        "outputId": "dbac7a61-883c-4ffd-a1f0-1d0e352d2320"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "oil production rate  224.6331191496763  kboe/day\n",
            "gas production rate  5.661286035687116  MS3/day\n",
            "LPG production rate  501.990509773624  tonns/day\n",
            "C5+ productionrate  149.36636517454994  Sm3/day\n"
          ]
        }
      ],
      "source": [
        "print('oil production rate ', oilkbblperday, ' kboe/day')\n",
        "print('gas production rate ', exportDryGas.getFlowRate('MSm3/day'), ' MS3/day')\n",
        "print('LPG production rate ', debutanizer.getGasOutStream().getFlowRate('kg/hr')*24.0/1e3 , ' tonns/day')\n",
        "print('C5+ productionrate ', debutanizer.getLiquidOutStream().getFlowRate('idSm3/hr')*24.0, ' Sm3/day')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0opUn6ekgjqz"
      },
      "source": [
        "## Product recovery"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qWQSTMVYcl88",
        "outputId": "7e5307fe-dd42-47a6-f0b1-3192e49918b9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "overall oil recovery  0.7225971583023082  kg produced/kg reservoir feed \n",
            "sales gas recovery  0.23713190889057567  kg produced/kg reservoir feed \n",
            "overall LPG recovery  0.026855837370538836  kg produced/kg reservoir feed \n",
            "overall C5+ recovery  0.005470257882875387  kg produced/kg reservoir feed \n",
            "sum recovery  0.9920551624462981\n"
          ]
        }
      ],
      "source": [
        "oilRecovery = overallORF\n",
        "richGasRecovery = 1.0 - oilRecovery\n",
        "salesgasrecovery = richGasRecovery*salesgasrecovery\n",
        "lpgrecovery = richGasRecovery*LPGrecovery\n",
        "c5plusrecovery = richGasRecovery*C5recovery\n",
        "print('overall oil recovery ', oilRecovery, ' kg produced/kg reservoir feed ')\n",
        "print('sales gas recovery ', salesgasrecovery, ' kg produced/kg reservoir feed ')\n",
        "print('overall LPG recovery ', lpgrecovery, ' kg produced/kg reservoir feed ')\n",
        "print('overall C5+ recovery ', c5plusrecovery, ' kg produced/kg reservoir feed ')\n",
        "\n",
        "print('sum recovery ', (oilRecovery+salesgasrecovery+lpgrecovery+c5plusrecovery))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aUBas_0OmxOD"
      },
      "source": [
        "## Product value"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HrhAPRcoekEj",
        "outputId": "1f08e70c-4cd2-4302-f8c5-c4b28795aba2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "oil revenue  3234.7169157553385  M US$/year\n",
            "gas revenue  2066.3694030257975  M US$/year\n",
            "lpg revenue  18.322653606737276  M US$/year\n",
            "C5+ revenue  3.7321361067446297  M US$/year\n"
          ]
        }
      ],
      "source": [
        "oilrevenue_annual = oilprice*oilkbblperday*1e3*streamdaysperyear/1e6 # MUS$\n",
        "gasrevenue_annual = salesgasprice*exportDryGas.getFlowRate('MSm3/day')*365 ## MUS$\n",
        "lpgrevenue_annual = LPGprice*debutanizer.getGasOutStream().getFlowRate('kg/hr')*24.0/1e3*365/1e6 ## MUS$\n",
        "c5plusrevenue_annual = C5price*debutanizer.getLiquidOutStream().getFlowRate('kg/hr')*24.0/1e3*365/1e6 ## US$\n",
        "\n",
        "print('oil revenue ' , oilrevenue_annual, ' M US$/year')\n",
        "print('gas revenue ' , gasrevenue_annual, ' M US$/year')\n",
        "print('lpg revenue ' , lpgrevenue_annual, ' M US$/year')\n",
        "print('C5+ revenue ' , c5plusrevenue_annual, ' M US$/year')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cph5UhsHos1y"
      },
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "38ovitReoubq"
      },
      "source": [
        "##CAPEX and year"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AWnfGQHVoxfp"
      },
      "source": [
        "....."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O67ucmYhoyxs"
      },
      "source": [
        "## Revenue and year"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vldkZhCFo3gK"
      },
      "source": [
        "....\n",
        "https://www.linkedin.com/pulse/how-build-oil-gas-financial-model-python-rishabh-sharma/"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PvVbxuzOo5Vh"
      },
      "source": [
        "##NPV/BE calculation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PhdkXDwZyL0p",
        "outputId": "7e89b394-7d06-43c2-dc28-78009f81b609"
      },
      "outputs": [
        {
          "data": {
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              "      <th></th>\n",
              "      <th>Year</th>\n",
              "      <th>oil</th>\n",
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              "      <th>LPG</th>\n",
              "      <th>C5plus</th>\n",
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              "      <th>taxgas_Revenue</th>\n",
              "      <th>taxLPG_Revenue</th>\n",
              "      <th>taxC5_Revenue</th>\n",
              "      <th>Cash Flow</th>\n",
              "      <th>Cumulative Cash Flows</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
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              "      <td>0.00000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>-1000000.00</td>\n",
              "      <td>-2000000.00</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2025</td>\n",
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              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
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              "      <td>-3000000.00</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2025</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1000000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000</td>\n",
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              "      <td>0.00000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>-1000000.00</td>\n",
              "      <td>-4000000.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2025</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
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              "      <td>0.000</td>\n",
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              "    <tr>\n",
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              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.00000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>-1000000.00</td>\n",
              "      <td>-6000000.00</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
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              "      <td>0.000</td>\n",
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              "      <td>-1000000.00</td>\n",
              "      <td>-7000000.00</td>\n",
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              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2028</td>\n",
              "      <td>224.633</td>\n",
              "      <td>224.633</td>\n",
              "      <td>224.633</td>\n",
              "      <td>24.633</td>\n",
              "      <td>0.0</td>\n",
              "      <td>200000.0</td>\n",
              "      <td>8985.32</td>\n",
              "      <td>224.633</td>\n",
              "      <td>22463.3</td>\n",
              "      <td>2463.3</td>\n",
              "      <td>2246.33</td>\n",
              "      <td>56.15825</td>\n",
              "      <td>5615.825</td>\n",
              "      <td>615.825</td>\n",
              "      <td>6738.99</td>\n",
              "      <td>-6993261.01</td>\n",
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              "    <tr>\n",
              "      <th>8</th>\n",
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              "      <td>224.633</td>\n",
              "      <td>224.633</td>\n",
              "      <td>224.633</td>\n",
              "      <td>24.633</td>\n",
              "      <td>0.0</td>\n",
              "      <td>200000.0</td>\n",
              "      <td>8985.32</td>\n",
              "      <td>224.633</td>\n",
              "      <td>22463.3</td>\n",
              "      <td>2463.3</td>\n",
              "      <td>2246.33</td>\n",
              "      <td>56.15825</td>\n",
              "      <td>5615.825</td>\n",
              "      <td>615.825</td>\n",
              "      <td>6738.99</td>\n",
              "      <td>-6986522.02</td>\n",
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              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2030</td>\n",
              "      <td>224.633</td>\n",
              "      <td>224.633</td>\n",
              "      <td>224.633</td>\n",
              "      <td>24.633</td>\n",
              "      <td>0.0</td>\n",
              "      <td>200000.0</td>\n",
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              "      <td>22463.3</td>\n",
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              "      <td>-6979783.03</td>\n",
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              "      <td>56.15825</td>\n",
              "      <td>5615.825</td>\n",
              "      <td>615.825</td>\n",
              "      <td>6738.99</td>\n",
              "      <td>-6973044.04</td>\n",
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              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-a3df8b92-a855-4c9d-ba28-1445dba8c0c7 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "    Year      oil      gas      LPG  C5plus      CAPEX      OPEX  Oil_Revenue  \\\n",
              "0   2023    0.000    0.000    0.000   0.000  1000000.0       0.0         0.00   \n",
              "1   2024    0.000    0.000    0.000   0.000  1000000.0       0.0         0.00   \n",
              "2   2025    0.000    0.000    0.000   0.000  1000000.0       0.0         0.00   \n",
              "3   2025    0.000    0.000    0.000   0.000  1000000.0       0.0         0.00   \n",
              "4   2025    0.000    0.000    0.000   0.000  1000000.0       0.0         0.00   \n",
              "5   2025    0.000    0.000    0.000   0.000  1000000.0       0.0         0.00   \n",
              "6   2027    0.000    0.000    0.000   0.000  1000000.0       0.0         0.00   \n",
              "7   2028  224.633  224.633  224.633  24.633        0.0  200000.0      8985.32   \n",
              "8   2029  224.633  224.633  224.633  24.633        0.0  200000.0      8985.32   \n",
              "9   2030  224.633  224.633  224.633  24.633        0.0  200000.0      8985.32   \n",
              "10  2040  224.633  224.633  224.633  24.633        0.0  200000.0      8985.32   \n",
              "\n",
              "    gas_Revenue  LPG_Revenue  C5_Revenue  taxOil_Revenue  taxgas_Revenue  \\\n",
              "0         0.000          0.0         0.0            0.00         0.00000   \n",
              "1         0.000          0.0         0.0            0.00         0.00000   \n",
              "2         0.000          0.0         0.0            0.00         0.00000   \n",
              "3         0.000          0.0         0.0            0.00         0.00000   \n",
              "4         0.000          0.0         0.0            0.00         0.00000   \n",
              "5         0.000          0.0         0.0            0.00         0.00000   \n",
              "6         0.000          0.0         0.0            0.00         0.00000   \n",
              "7       224.633      22463.3      2463.3         2246.33        56.15825   \n",
              "8       224.633      22463.3      2463.3         2246.33        56.15825   \n",
              "9       224.633      22463.3      2463.3         2246.33        56.15825   \n",
              "10      224.633      22463.3      2463.3         2246.33        56.15825   \n",
              "\n",
              "    taxLPG_Revenue  taxC5_Revenue   Cash Flow  Cumulative Cash Flows  \n",
              "0            0.000          0.000 -1000000.00            -1000000.00  \n",
              "1            0.000          0.000 -1000000.00            -2000000.00  \n",
              "2            0.000          0.000 -1000000.00            -3000000.00  \n",
              "3            0.000          0.000 -1000000.00            -4000000.00  \n",
              "4            0.000          0.000 -1000000.00            -5000000.00  \n",
              "5            0.000          0.000 -1000000.00            -6000000.00  \n",
              "6            0.000          0.000 -1000000.00            -7000000.00  \n",
              "7         5615.825        615.825     6738.99            -6993261.01  \n",
              "8         5615.825        615.825     6738.99            -6986522.02  \n",
              "9         5615.825        615.825     6738.99            -6979783.03  \n",
              "10        5615.825        615.825     6738.99            -6973044.04  "
            ]
          },
          "execution_count": 77,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "productionprofile = {'Year':  [2023,  2024, 2025, 2025, 2027, 2028, 2029, 2030, 2040],\n",
        "        'oil':                [0,   0,  0,  0,  0,  224.633,  224.633, 224.633, 224.633],\n",
        "        'gas':                [0,   0,  0,  0,  0,  224.633,  224.633, 224.633, 224.633],\n",
        "        'LPG':                [0,   0,  0,  0,  0,  224.633,  224.633, 224.633, 224.633],\n",
        "        'C5plus':             [0,   0,  0,  0,  0,  24.633,  24.633, 24.633, 24.633],\n",
        "  }\n",
        "\n",
        "\n",
        "investmentprofile = {'Year':  [2023,  2024, 2025, 2025, 2027, 2028, 2029, 2030, 2040],\n",
        "        'CAPEX':                [1e6,   1e6,  1e6,  1e6,  1e6,  0,  0, 0, 0],\n",
        "        'OPEX':                 [0, 0, 0, 0, 0, 200e3, 200e3,200e3,200e3],\n",
        "  }\n",
        "\n",
        "prodframe = pd.DataFrame(productionprofile)\n",
        "investframe = pd.DataFrame(investmentprofile)\n",
        "\n",
        "mergtab = pd.merge(prodframe, investframe, how='outer', on='Year')\n",
        "\n",
        "mergtab['Oil_Revenue'] = oilprice * mergtab['oil']\n",
        "mergtab['gas_Revenue'] = salesgasprice * mergtab['gas']\n",
        "mergtab['LPG_Revenue'] = LPGprice * mergtab['LPG']\n",
        "mergtab['C5_Revenue'] = C5price * mergtab['C5plus']\n",
        "\n",
        "mergtab['taxOil_Revenue'] = taxoil * mergtab['Oil_Revenue']\n",
        "mergtab['taxgas_Revenue'] = taxgas * mergtab['gas_Revenue']\n",
        "mergtab['taxLPG_Revenue'] = taxLPG * mergtab['LPG_Revenue']\n",
        "mergtab['taxC5_Revenue'] = taxC5 * mergtab['C5_Revenue']\n",
        "\n",
        "\n",
        "mergtab['Cash Flow'] = mergtab['Oil_Revenue'] - mergtab['taxOil_Revenue'] - mergtab['CAPEX']\n",
        "mergtab['Cumulative Cash Flows'] = np.cumsum(mergtab['Cash Flow'])\n",
        "\n",
        "mergtab"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o9ynoFqxo8b5"
      },
      "source": [
        "NPV is calculated using the formula:\n",
        "\n",
        "$NPV=\\Sigma\\frac{CF_t}{(1+r)^t}$\n",
        "\n",
        "$CF_t$ - Net cash flow at time t\n",
        "\n",
        "N - Total number of Periods\n",
        "\n",
        "r - Discount rate or rate of return"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hsIj_BZy45gB",
        "outputId": "ea0f3d7c-7ef3-4762-b9f7-d32132a957ec"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "NPV : $ -5343202.581\n"
          ]
        }
      ],
      "source": [
        "cash_flow = mergtab['Cash Flow'].values\n",
        "mergtab['Year'] = [i for i in range(11)]\n",
        "t = mergtab['Year'].values\n",
        "\n",
        "def npv(irr, cashfs, t):\n",
        "    x =  np.sum(cashfs / (1 + irr) ** t)\n",
        "    return round(x, 3)\n",
        "\n",
        "\n",
        "print(\"NPV : $\", npv(irr=0.10, cashfs=cash_flow, t=t))"
      ]
    }
  ],
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