EnP_training/SPE_assignment3_notebook.ipynb

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        "<a href=\"https://colab.research.google.com/github/yohanesnuwara/python-bootcamp-for-geoengineers/blob/master/EnP_training/SPE_assignment3_notebook.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "id": "mYF9TAAYHktD"
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      "source": [
        "# Assignment 3\n",
        "\n",
        "This assignment is about the application of Python for well-test analysis. You're given a CSV data (have been loaded here), and your task is to **simulate the well transient response** (not to analyze it).\n",
        "\n",
        "### Given reservoir properties\n",
        "\n",
        "* Reservoir initial pressure 3,000 psia\n",
        "* Porosity 15%\n",
        "* Total compressibility 12 microsip\n",
        "* Permeability 1,000 md\n",
        "* Radius of wellbore 4 inch (1 inch = 0.0833 ft)\n",
        "* Reservoir thickness 10 ft\n",
        "* Oil viscosity 2 cp\n",
        "* Distance from wellbore to reservoir outer boundary 8,000 ft \n",
        "* Oil FVF 1.33 RB/STB\n",
        "\n",
        "### Read instruction carefully 😊\n",
        "\n",
        "* To help you coding, refer to the section about \"Modeling a Multirate Test\" and \"Modeling a Multiple Pressure Test\" in our [session 5 notebook](https://bit.ly/python-welltest).\n",
        "\n",
        "* There are 3 columns in the data, \"Time\" (in hours), \"Rate\" (in $m^3$/hour), and \"Pressure\" (in bar). \n",
        "\n",
        "* Don't forget to convert the \"Rate\" to barrel/day and convert \"Pressure\" to psia. \n",
        "\n",
        "* Do a modeling of multirate test using the \"Time\" and \"Rate\" data\n",
        "* Do a modeling of multiple pressure test using the \"Time\" and \"Pressure\" data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I9zHGyR8HgiM"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
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        "id": "FRAYMu5iJx4t",
        "outputId": "d7b2cf5d-0e61-4fb8-8615-96e4044567e3"
      },
      "source": [
        "# cloning to github repository \n",
        "!git clone https://github.com/yohanesnuwara/pyreservoir"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'pyreservoir'...\n",
            "remote: Enumerating objects: 161, done.\u001b[K\n",
            "remote: Counting objects: 100% (161/161), done.\u001b[K\n",
            "remote: Compressing objects: 100% (161/161), done.\u001b[K\n",
            "remote: Total 819 (delta 85), reused 0 (delta 0), pack-reused 658\u001b[K\n",
            "Receiving objects: 100% (819/819), 24.03 MiB | 14.12 MiB/s, done.\n",
            "Resolving deltas: 100% (439/439), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AZZRzpXYLCHx"
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      "source": [
        "# import system\n",
        "import sys\n",
        "sys.path.append('/content/pyreservoir/welltest')\n",
        "\n",
        "# functions you will use\n",
        "from wellflo import * # import all functions\n",
        "from wellanalysis import constant_rate_drawdown_test, constant_rate_buildup_test"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QaEcq2baJ5Nx"
      },
      "source": [
        "# filepath of the data\n",
        "filepath = '/content/pyreservoir/data/welltest/Data_Well_Transient_Modeling.csv'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
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        "outputId": "febe1c0d-0478-4e50-8249-30d42c7d0d2c"
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      "source": [
        "Data = pd.read_csv(filepath)\n",
        "Data[\"pressure (bar)\"] = Data[\"pressure (bar)\"].apply(lambda x: x*14.504)\n",
        "Data[\"rate (m3/hr)\"] = Data[\"rate (m3/hr)\"].apply(lambda x: x*150.955)\n",
        "Data = Data.rename(columns={'rate (m3/hr)':'rate(bbl/day)','pressure (bar)':'pressure(psia)'})\n",
        "Data"
      ],
      "execution_count": null,
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              "    time (hr)  rate(bbl/day)  pressure(psia)\n",
              "0           0        483.056     2000.000072\n",
              "1          10        483.056     2000.000072\n",
              "2          20        483.056     2000.000072\n",
              "3          30        483.056     2000.000072\n",
              "4          40        483.056     2000.000072\n",
              "..        ...            ...             ...\n",
              "94        940        150.955     2025.004968\n",
              "95        950        150.955     2025.004968\n",
              "96        960        150.955     2025.004968\n",
              "97        970        150.955     2025.004968\n",
              "98        980        150.955     2025.004968\n",
              "\n",
              "[99 rows x 3 columns]"
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    {
      "cell_type": "markdown",
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        "id": "rbmwC1vxQqrz"
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      "source": [
        "## Input reservoir properties"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c-38ojccQtJo"
      },
      "source": [
        "# Input reservoir properties\n",
        "poro = 0.15 \n",
        "ct = 12E-6 \n",
        "perm = 1000\n",
        "rw = 4 * 0.08333 \n",
        "h = 10\n",
        "mu = 2 \n",
        "re = 8000\n",
        "Bo = 1.33\n",
        "\n",
        "p_initial = 3000\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fw5gUhIAQUp1"
      },
      "source": [
        "## Modeling the Multirate Test (use timestep 0.1 hour)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4zdGaBf090_3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        },
        "outputId": "11c17cc2-baea-449c-c1ea-80576dd4a5c8"
      },
      "source": [
        "Data.head(10)"
      ],
      "execution_count": null,
      "outputs": [
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              "      <th>time (hr)</th>\n",
              "      <th>rate(bbl/day)</th>\n",
              "      <th>pressure(psia)</th>\n",
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              "   time (hr)  rate(bbl/day)  pressure(psia)\n",
              "0          0        483.056     2000.000072\n",
              "1         10        483.056     2000.000072\n",
              "2         20        483.056     2000.000072\n",
              "3         30        483.056     2000.000072\n",
              "4         40        483.056     2000.000072\n",
              "5         50        483.056     2000.000072\n",
              "6         60        483.056     2000.000072\n",
              "7         70        603.820     2000.000072\n",
              "8         80        603.820     2000.000072\n",
              "9         90        603.820     2000.000072"
            ]
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      "metadata": {
        "id": "I25OTQlXQXbG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "6fedf8d3-8114-4054-de84-63718e11e7f1"
      },
      "source": [
        "# Define t and q from data\n",
        "t_change = Data['time (hr)'].values\n",
        "q_change = Data['rate(bbl/day)'].values\n",
        "\n",
        "\n",
        "plt.step(t_change,q_change)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2xeyjC9j-1kY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a6f1a00f-38a5-4a6f-a74c-1b5994f1af4f"
      },
      "source": [
        "time_finite = time_finite_acting(perm,poro,mu,ct,rw,re)\n",
        "time_finite = np.round(time_finite,3)\n",
        "print('Finite acting time is {} hrs'.format(time_finite))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Finite acting time is 218.43 hrs\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l288_DNd-1MY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "outputId": "e91093d7-c7ba-48bf-99a6-5212ea59ea3a"
      },
      "source": [
        "\n",
        "\n",
        "t_step = 0.1\n",
        "\n",
        "simulate_multirate_test(p_initial, t_step, t_change, q_change, re, rw, perm, poro, mu, ct, Bo, h)                        \n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1224x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iF8bPd8BQXr8"
      },
      "source": [
        "## Modeling the Multiple Pressure Test (use timestep 0.1 hour)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1Yy6QH-OQZ_g"
      },
      "source": [
        "# Define t and p from data\n",
        "poro = 0.15 \n",
        "ct = 12E-6 \n",
        "perm = 1000\n",
        "rw = 4 * 0.08333 \n",
        "h = 10\n",
        "mu = 2 \n",
        "re = 8000\n",
        "Bo = 1.33\n",
        "\n",
        "p_initial = 3000\n",
        "\n",
        "\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "90b1_X0xPe2v",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "c875f4f3-05cd-4229-d4d4-321c8f8ed548"
      },
      "source": [
        "t_change = Data['time (hr)'].values\n",
        "p_change = Data['pressure(psia)'].values\n",
        "\n",
        "\n",
        "plt.step(t_change,p_change)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HrqgU070PepQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e2489a45-1054-4720-9ed0-0c397e7dac84"
      },
      "source": [
        "time_finite_multipsi = time_finite_acting(perm,poro,mu,ct,rw,re)\n",
        "time_finite_multipsi = np.round(time_finite_multipsi,3)\n",
        "print('Finite acting time is {} hrs'.format(time_finite_multipsi))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Finite acting time is 218.43 hrs\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JDYQccSIRAxV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "outputId": "c9386ab5-dc14-497b-8f5d-0c03f12f039b"
      },
      "source": [
        "t_step = 0.1\n",
        "\n",
        "simulate_multipressure_test(p_initial, t_step, t_change, p_change, \n",
        "                            re, rw, perm, poro, mu, ct, Bo, h)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1224x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-cIasmFzRAhD"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YBYg-vH5Q_58"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}