simulators/teasers/PyReSim_teaser2.ipynb

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\n",
                "text/plain": "<Figure size 720x432 with 2 Axes>"
              }
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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/yohanesnuwara/pyresim/blob/master/simulators/teasers/PyReSim_teaser2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dPLOUQgN9ePA",
        "colab_type": "text"
      },
      "source": [
        "## PyReSim Teaser #2 Slightly Compressible Simulation in Reservoir with Irregular Geometry"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q3IWovMHSm47",
        "colab_type": "text"
      },
      "source": [
        "Read the full description of Teaser 2 case [here](https://github.com/yohanesnuwara/pyresim/blob/master/docs/teaser.md#teaser-2)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J-Sf8NNT3kbo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HrKrhulN4urc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 138
        },
        "outputId": "fe854373-bf0f-4af2-fb9a-182305fc4504"
      },
      "source": [
        "!git clone https://github.com/yohanesnuwara/pyresim"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'pyresim'...\n",
            "remote: Enumerating objects: 111, done.\u001b[K\n",
            "remote: Counting objects:   0% (1/111)\u001b[K\rremote: Counting objects:   1% (2/111)\u001b[K\rremote: Counting objects:   2% (3/111)\u001b[K\rremote: Counting objects:   3% (4/111)\u001b[K\rremote: Counting objects:   4% (5/111)\u001b[K\rremote: Counting objects:   5% (6/111)\u001b[K\rremote: Counting objects:   6% (7/111)\u001b[K\rremote: Counting objects:   7% (8/111)\u001b[K\rremote: Counting objects:   8% (9/111)\u001b[K\rremote: Counting objects:   9% (10/111)\u001b[K\rremote: Counting objects:  10% (12/111)\u001b[K\rremote: Counting objects:  11% (13/111)\u001b[K\rremote: Counting objects:  12% (14/111)\u001b[K\rremote: Counting objects:  13% (15/111)\u001b[K\rremote: Counting objects:  14% (16/111)\u001b[K\rremote: Counting objects:  15% (17/111)\u001b[K\rremote: Counting objects:  16% (18/111)\u001b[K\rremote: Counting objects:  17% (19/111)\u001b[K\rremote: Counting objects:  18% (20/111)\u001b[K\rremote: Counting objects:  19% (22/111)\u001b[K\rremote: Counting 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            "remote: Compressing objects:   0% (1/110)\u001b[K\rremote: Compressing objects:   1% (2/110)\u001b[K\rremote: Compressing objects:   2% (3/110)\u001b[K\rremote: Compressing objects:   3% (4/110)\u001b[K\rremote: Compressing objects:   4% (5/110)\u001b[K\rremote: Compressing objects:   5% (6/110)\u001b[K\rremote: Compressing objects:   6% (7/110)\u001b[K\rremote: Compressing objects:   7% (8/110)\u001b[K\rremote: Compressing objects:   8% (9/110)\u001b[K\rremote: Compressing objects:   9% (10/110)\u001b[K\rremote: Compressing objects:  10% (11/110)\u001b[K\rremote: Compressing objects:  11% (13/110)\u001b[K\rremote: Compressing objects:  12% (14/110)\u001b[K\rremote: Compressing objects:  13% (15/110)\u001b[K\rremote: Compressing objects:  14% (16/110)\u001b[K\rremote: Compressing objects:  15% (17/110)\u001b[K\rremote: Compressing objects:  16% (18/110)\u001b[K\rremote: Compressing objects:  17% (19/110)\u001b[K\rremote: Compressing objects:  18% (20/110)\u001b[K\rremote: 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            "Receiving objects:   0% (1/1142)   \rReceiving objects:   1% (12/1142)   \rReceiving objects:   2% (23/1142)   \rReceiving objects:   3% (35/1142)   \rReceiving objects:   4% (46/1142)   \rReceiving objects:   5% (58/1142)   \rReceiving objects:   6% (69/1142)   \rReceiving objects:   7% (80/1142)   \rReceiving objects:   8% (92/1142)   \rReceiving objects:   9% (103/1142)   \rReceiving objects:  10% (115/1142)   \rReceiving objects:  11% (126/1142)   \rReceiving objects:  12% (138/1142)   \rReceiving objects:  13% (149/1142)   \rReceiving objects:  14% (160/1142)   \rReceiving objects:  15% (172/1142)   \rReceiving objects:  16% (183/1142)   \rReceiving objects:  17% (195/1142)   \rReceiving objects:  18% (206/1142)   \rReceiving objects:  19% (217/1142)   \rReceiving objects:  20% (229/1142)   \rReceiving objects:  21% (240/1142)   \rReceiving objects:  22% (252/1142)   \rReceiving objects:  23% (263/1142)   \rReceiving objects:  24% (275/1142)   \rReceiving objects:  25% (286/1142)   \rReceiving objects:  26% (297/1142)   \rReceiving objects:  27% (309/1142)   \rReceiving objects:  28% (320/1142)   \rReceiving objects:  29% (332/1142)   \rReceiving objects:  30% (343/1142)   \rReceiving objects:  31% (355/1142)   \rReceiving objects:  32% (366/1142)   \rReceiving objects:  33% (377/1142)   \rReceiving objects:  34% (389/1142)   \rReceiving objects:  35% (400/1142)   \rReceiving objects:  36% (412/1142)   \rReceiving objects:  37% (423/1142)   \rReceiving objects:  38% (434/1142)   \rReceiving objects:  39% (446/1142)   \rReceiving objects:  40% (457/1142)   \rReceiving objects:  41% (469/1142)   \rReceiving objects:  42% (480/1142)   \rReceiving objects:  43% (492/1142)   \rReceiving objects:  44% (503/1142)   \rReceiving objects:  45% (514/1142)   \rReceiving objects:  46% (526/1142)   \rReceiving objects:  47% (537/1142)   \rReceiving objects:  48% (549/1142)   \rReceiving objects:  49% (560/1142)   \rReceiving objects:  50% (571/1142)   \rReceiving objects:  51% (583/1142)   \rReceiving objects:  52% (594/1142)   \rReceiving objects:  53% (606/1142)   \rReceiving objects:  54% (617/1142)   \rReceiving objects:  55% (629/1142)   \rReceiving objects:  56% (640/1142)   \rReceiving objects:  57% (651/1142)   \rReceiving objects:  58% (663/1142)   \rReceiving objects:  59% (674/1142)   \rReceiving objects:  60% (686/1142)   \rReceiving objects:  61% (697/1142)   \rReceiving objects:  62% (709/1142)   \rReceiving objects:  63% (720/1142)   \rReceiving objects:  64% (731/1142)   \rReceiving objects:  65% (743/1142)   \rReceiving objects:  66% (754/1142)   \rReceiving objects:  67% (766/1142)   \rReceiving objects:  68% (777/1142)   \rReceiving objects:  69% (788/1142)   \rReceiving objects:  70% (800/1142)   \rReceiving objects:  71% (811/1142)   \rReceiving objects:  72% (823/1142)   \rReceiving objects:  73% (834/1142)   \rReceiving objects:  74% (846/1142)   \rReceiving objects:  75% (857/1142)   \rReceiving objects:  76% (868/1142)   \rReceiving objects:  77% (880/1142)   \rReceiving objects:  78% (891/1142)   \rReceiving objects:  79% (903/1142)   \rReceiving objects:  80% (914/1142)   \rReceiving objects:  81% (926/1142)   \rReceiving objects:  82% (937/1142)   \rReceiving objects:  83% (948/1142)   \rReceiving objects:  84% (960/1142)   \rReceiving objects:  85% (971/1142)   \rReceiving objects:  86% (983/1142)   \rReceiving objects:  87% (994/1142)   \rReceiving objects:  88% (1005/1142)   \rReceiving objects:  89% (1017/1142)   \rReceiving objects:  90% (1028/1142)   \rReceiving objects:  91% (1040/1142)   \rReceiving objects:  92% (1051/1142)   \rReceiving objects:  93% (1063/1142)   \rReceiving objects:  94% (1074/1142)   \rremote: Total 1142 (delta 66), reused 0 (delta 0), pack-reused 1031\u001b[K\n",
            "Receiving objects:  95% (1085/1142)   \rReceiving objects:  96% (1097/1142)   \rReceiving objects:  97% (1108/1142)   \rReceiving objects:  98% (1120/1142)   \rReceiving objects:  99% (1131/1142)   \rReceiving objects: 100% (1142/1142)   \rReceiving objects: 100% (1142/1142), 471.12 KiB | 11.22 MiB/s, done.\n",
            "Resolving deltas:   0% (0/691)   \rResolving deltas:   9% (67/691)   \rResolving deltas:  10% (74/691)   \rResolving deltas:  11% (79/691)   \rResolving deltas:  12% (84/691)   \rResolving deltas:  16% (115/691)   \rResolving deltas:  19% (134/691)   \rResolving deltas:  20% (142/691)   \rResolving deltas:  21% (150/691)   \rResolving deltas:  23% (160/691)   \rResolving deltas:  24% (170/691)   \rResolving deltas:  25% (176/691)   \rResolving deltas:  26% (182/691)   \rResolving deltas:  30% (211/691)   \rResolving deltas:  31% (215/691)   \rResolving deltas:  32% (226/691)   \rResolving deltas:  34% (239/691)   \rResolving deltas:  35% (248/691)   \rResolving deltas:  36% (249/691)   \rResolving deltas:  37% (256/691)   \rResolving deltas:  39% (270/691)   \rResolving deltas:  40% (281/691)   \rResolving deltas:  41% (289/691)   \rResolving deltas:  42% (295/691)   \rResolving deltas:  43% (298/691)   \rResolving deltas:  47% (330/691)   \rResolving deltas:  48% (332/691)   \rResolving deltas:  49% (339/691)   \rResolving deltas:  62% (430/691)   \rResolving deltas:  66% (461/691)   \rResolving deltas:  67% (464/691)   \rResolving deltas:  68% (471/691)   \rResolving deltas:  69% (479/691)   \rResolving deltas:  70% (484/691)   \rResolving deltas:  71% (492/691)   \rResolving deltas:  72% (499/691)   \rResolving deltas:  73% (505/691)   \rResolving deltas:  74% (515/691)   \rResolving deltas:  80% (558/691)   \rResolving deltas:  85% (592/691)   \rResolving deltas:  86% (598/691)   \rResolving deltas:  87% (602/691)   \rResolving deltas:  88% (610/691)   \rResolving deltas:  92% (642/691)   \rResolving deltas:  94% (653/691)   \rResolving deltas:  95% (657/691)   \rResolving deltas:  96% (666/691)   \rResolving deltas:  97% (672/691)   \rResolving deltas:  98% (681/691)   \rResolving deltas:  99% (685/691)   \rResolving deltas: 100% (691/691)   \rResolving deltas: 100% (691/691), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lmU_Khbj4zAc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import sys\n",
        "sys.path.append('/content/pyresim/functions')"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ovm13Nqb46Lh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from input_output import read_input\n",
        "from boundary import boundary2d_location, boundary_flow2d_constant_pressuregrad, boundary_floweq2d\n",
        "from gridding import fill_active_blocks, source2d, create_irregular_grid, maskout_inactive_blocks\n",
        "from solver import lhs_coeffs2d_welltype, rhs_constant2d_welltype, fill2d_lhs_mat, fill2d_rhs_mat, solve_pressure_irregular2d\n",
        "from transmissibility import transmissibility2d, transmissibility2d_boundary\n",
        "from wellblock import prodrate1d, fraction_wellblock_geometric_factor\n",
        "from wellsimulation import solution_well2d"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7XZCtQ0o_lPN",
        "colab_type": "text"
      },
      "source": [
        "Additional functions for Slightly Compressible simulation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EWQOUBHY_lZ7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from wellsimulation import well2d_report_slicomp\n",
        "from synthetics import synthetic_initial_pressure2d"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qe1AGKBS-t9J",
        "colab_type": "text"
      },
      "source": [
        "## Slightly Compressible simulation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dq_WegDm7ux2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import warnings\n",
        "\n",
        "warnings.filterwarnings(\"ignore\", category=FutureWarning)\n",
        "\n",
        "\"\"\"\"\"\"\"\"\"\"\"\n",
        "PUT THE INPUT TXT DATA\n",
        "\"\"\"\"\"\"\"\"\"\"\"\n",
        "\n",
        "filepath = '/content/pyresim/input/teasers/teaser2_input.txt'\n",
        "\n",
        "# call the read_input function to read input data\n",
        "reservoir_input, wells, west_boundary, east_boundary, south_boundary, north_boundary = read_input(filepath)\n",
        "\n",
        "\"\"\"\"\"\"\"\"\"\"\"\n",
        "SLIGHTLY INCOMPRESSIBLE INPUTS\n",
        "\"\"\"\"\"\"\"\"\"\"\"\n",
        "\n",
        "# initialize pressure (if you don't have data, create your own synthetic data,\n",
        "# using function 'synthetic_initial_pressure2d'. In this case, set 4,000 psi.\n",
        "p_initial = synthetic_initial_pressure2d(reservoir_input['xi'], reservoir_input['yi'],\n",
        "                                         function='equilibrize', p_ref=4000)\n",
        "\n",
        "timestep = 1 # time increment, day\n",
        "schedule = 50 # how long will be simulated, days\n",
        "\n",
        "\"\"\"\"\"\"\"\"\"\"\"\n",
        "IRREGULARITIES\n",
        "\"\"\"\"\"\"\"\"\"\"\"\n",
        "\n",
        "# define grid point location of inactive blocks\n",
        "xy_inactive = [(1,1), (1,6), (1,7), (1,8), (1,9),\n",
        "               (2,6), (2,7), (2,8), (2,9),\n",
        "               (3,6), (3,7), (3,8), (3,9),\n",
        "               (4,7), (4,8), (4,9),\n",
        "               (5,1), (5,7), (5,8), (5,9),\n",
        "               (6,1), (6,2), (6,8), (6,9),\n",
        "               (7,1), (7,2),\n",
        "               (8,1), (8,2),\n",
        "               (9,1), (9,7),\n",
        "               (10,1), (10,7), (10,8), (10,9),\n",
        "               (11,1), (11,2), (11,5), (11,6), (11,7), (11,8), (11,9),\n",
        "               (12,1), (12,2), (12,5), (12,6), (12,7), (12,8), (12,9)]\n",
        "\n",
        "\"\"\"\"\"\"\"\"\"\"\"\n",
        "INPUT PROCESSING\n",
        "\"\"\"\"\"\"\"\"\"\"\"\n",
        "\n",
        "# number of blocks in x and y\n",
        "xi = reservoir_input['xi']\n",
        "yi = reservoir_input['yi']\n",
        "\n",
        "# define parameters, in this case blocks are homogeneous and same in size\n",
        "dx = reservoir_input['dx'] \n",
        "dy = reservoir_input['dy']\n",
        "dz = reservoir_input['dz']\n",
        "kx = reservoir_input['kx']\n",
        "ky = reservoir_input['ky']\n",
        "B = reservoir_input['B']\n",
        "mu = reservoir_input['mu']\n",
        "\n",
        "# well information (dataframe)\n",
        "well_name = wells['well_name']\n",
        "well_rw = wells['well_rw'] / 12  # wellbore radius, inch to ft\n",
        "well_skin = wells['well_skin']\n",
        "well_loc = wells['well_loc']\n",
        "well_condition = wells['well_condition']\n",
        "well_value = wells['well_value']\n",
        "well_config = wells['well_config']\n",
        "\n",
        "\"\"\"\"\"\"\"\"\"\"\"\n",
        "GRIDDING\n",
        "\"\"\"\"\"\"\"\"\"\"\"\n",
        "\n",
        "\" Create grid points \"\n",
        "# meshgrid the original points\n",
        "x_ = np.arange(1, xi + 1)\n",
        "y_ = np.arange(1, yi + 1)\n",
        "\n",
        "x, y = np.meshgrid(x_, y_, indexing='ij')\n",
        "\n",
        "\" Classify the location of boundary with codes (1, 12, 13, etc) \"\n",
        "bound_loc = boundary2d_location(x, y, xi, yi)\n",
        "\n",
        "\" For Slightly Incompressible sim, there are information of CFLUID and CPORE \"\n",
        "\n",
        "# convert unit microsip to sip (* 10^-6)\n",
        "reservoir_input['cfluid'] = reservoir_input['cfluid'] * 1E-6\n",
        "reservoir_input['cpore'] = reservoir_input['cpore'] * 1E-6\n",
        "\n",
        "\"\"\"\"\"\"\"\"\"\"\"\n",
        "IRREGULARITIES\n",
        "\"\"\"\"\"\"\"\"\"\"\"\n",
        "\n",
        "\" Create irregular grids \"\n",
        "x, y, x_inactive, y_inactive = create_irregular_grid(x, y, xy_inactive)\n",
        "\n",
        "\" Mask out initial pressure distribution from INACTIVE BLOCKS \"\n",
        "p_initial = maskout_inactive_blocks(p_initial, x_inactive, y_inactive, xi)\n",
        "\n",
        "\" Mask out the boundary codes produced before, for INACTIVE BLOCKS \"\n",
        "bound_loc = maskout_inactive_blocks(bound_loc.T, x_inactive, y_inactive, xi)\n",
        "      \n",
        "\" Classify the NEW location of boundary AFTER being masked out from INACTIVE BLOCKS \"\n",
        "from boundary import boundary2d_irreg\n",
        "\n",
        "bound_loc = boundary2d_irreg(bound_loc, xi, yi) \n",
        "\n",
        "\"\"\"\"\"\"\"\"\"\"\"\n",
        "WELL INFORMATION PROCESSING\n",
        "\"\"\"\"\"\"\"\"\"\"\"\n",
        "\n",
        "# source block (production or injection well)\n",
        "qsc = source2d(well_value, np.array(well_loc), xi, yi)  # call function HERE\n",
        "\n",
        "# Calculate wellblock geometric factor\n",
        "kh, r_eq, Gw = [], [], []\n",
        "\n",
        "for i in range(len(well_config)):\n",
        "    kh_, r_eq_, Gw_ = fraction_wellblock_geometric_factor(dx, dy, kx, ky, well_skin[i],\n",
        "                                                          well_rw[i], dz, well_config[i])\n",
        "    kh.append(kh_);\n",
        "    r_eq.append(r_eq_);\n",
        "    Gw.append(Gw_)\n",
        "\n",
        "# create WELLBLOCK PROPERTY \n",
        "# inject (fill) the operating wells to 'qsc' grid that we created before\n",
        "# the values won't be filled in the 'qsc' grid that has value = 0\n",
        "qsc = qsc.astype('float')\n",
        "qsc[qsc == 0] = np.nan\n",
        "\n",
        "well_value = qsc\n",
        "well_condition = fill_active_blocks(well_condition, qsc)\n",
        "well_rw = fill_active_blocks(well_rw, qsc)\n",
        "well_Gw = fill_active_blocks(np.array(Gw), qsc)\n",
        "well_mu = fill_active_blocks(np.array(mu), qsc)\n",
        "well_B = fill_active_blocks(np.array(B), qsc)\n",
        "\n",
        "\"\"\"\"\"\"\"\"\"\"\"\n",
        "SIMULATION\n",
        "\"\"\"\"\"\"\"\"\"\"\"\n",
        "\n",
        "def lookup(i, j, bound_dict):\n",
        "    \"\"\"\n",
        "    Determine the type and value of boundary at a grid block\n",
        "    given the boundary dictionary\n",
        "    \"\"\"\n",
        "    if bound_dict['loc'] == 'all':\n",
        "        bound_type = bound_dict['type']\n",
        "        bound_value = bound_dict['value']\n",
        "    else:\n",
        "        x, y = i + 1, j + 1\n",
        "\n",
        "        id1 = (np.where(np.all(bound_dict['loc'] == np.array([x, y]), axis=-1)))[0][0]\n",
        "        id2 = (np.where(np.all((bound_dict['loc'][id1]) == np.array([x, y]), axis=-1)))[0][0]\n",
        "\n",
        "        bound_type = (bound_dict['type'])[id1]\n",
        "        bound_value = (bound_dict['value'])[id1]\n",
        "    return bound_type, bound_value\n",
        "\n",
        "# Create block index\n",
        "block_index = np.arange(1, (xi * yi) + 1)\n",
        "block_index = (np.reshape(block_index, (-1, xi))).T\n",
        "\n",
        "# Create empty matrix for LHS and RHS\n",
        "lhs_mat = np.array([[0.] * (xi * yi)] * (xi * yi))\n",
        "\n",
        "# set up empty array for well and boundary dictionary of each block \n",
        "# for later computation of RHS and pressure solve at each timestep\n",
        "well_dict = np.array([[0.] * yi] * xi).astype(dict)\n",
        "boundary_dict = np.array([[0.] * yi] * xi).astype(dict)\n",
        "\n",
        "\" Start simulation \"\n",
        "\n",
        "for i in range(xi):\n",
        "    for j in range(yi):\n",
        "\n",
        "      \"\"\"\"\"\"\"\"\"\n",
        "      ACTIVE BLOCKS\n",
        "      \"\"\"\"\"\"\"\"\"\n",
        "      if bound_loc[i,j] == bound_loc[i,j]:      \n",
        "\n",
        "        if bound_loc[i, j] == 13:\n",
        "            \"\"\"\"\"\"\"\"\"\"\"\n",
        "            MODIFY THIS W.R.T. THE GRID BLOCK LOCATION \n",
        "            \"\"\"\"\"\"\"\"\"\"\"\n",
        "\n",
        "            # get the TYPE and VALUE of the boundary from the BOUNDARY DICTIONARY\n",
        "            b1_type, b1_value = lookup(i, j, west_boundary)\n",
        "            b3_type, b3_value = lookup(i, j, south_boundary)\n",
        "\n",
        "            # create boundary dictionary\n",
        "            boundary = {'loc': np.array([1, 3]),\n",
        "                        'type': np.array([b1_type, b3_type]),\n",
        "                        'value': np.array([b1_value, b3_value])}\n",
        "\n",
        "            # create non-boundary dictionary\n",
        "            non_boundary = {'loc': np.array([2, 4])}\n",
        "\n",
        "            # create well dictionary\n",
        "            well = {'condition': well_condition[i, j],\n",
        "                    'value': well_value[i, j],\n",
        "                    'rw': well_rw[i, j],\n",
        "                    'Gw': well_Gw[i, j]}\n",
        "\n",
        "        if bound_loc[i, j] == 14:\n",
        "            \"\"\"\"\"\"\"\"\"\"\"\n",
        "            MODIFY THIS W.R.T. THE GRID BLOCK LOCATION \n",
        "            \"\"\"\"\"\"\"\"\"\"\"\n",
        "\n",
        "            # get the TYPE and VALUE of the boundary from the BOUNDARY DICTIONARY\n",
        "            b1_type, b1_value = lookup(i, j, west_boundary)\n",
        "            b4_type, b4_value = lookup(i, j, north_boundary)\n",
        "\n",
        "            # create boundary dictionary\n",
        "            boundary = {'loc': np.array([1, 4]),\n",
        "                        'type': np.array([b1_type, b4_type]),\n",
        "                        'value': np.array([b1_value, b4_value])}\n",
        "\n",
        "            # create non-boundary dictionary\n",
        "            non_boundary = {'loc': np.array([2, 3])}\n",
        "\n",
        "            # create well dictionary\n",
        "            well = {'condition': well_condition[i, j],\n",
        "                    'value': well_value[i, j],\n",
        "                    'rw': well_rw[i, j],\n",
        "                    'Gw': well_Gw[i, j]}\n",
        "\n",
        "        if bound_loc[i, j] == 23:\n",
        "            \"\"\"\"\"\"\"\"\"\"\"\n",
        "            MODIFY THIS W.R.T. THE GRID BLOCK LOCATION \n",
        "            \"\"\"\"\"\"\"\"\"\"\"\n",
        "\n",
        "            # get the TYPE and VALUE of the boundary from the BOUNDARY DICTIONARY\n",
        "            b2_type, b2_value = lookup(i, j, east_boundary)\n",
        "            b3_type, b3_value = lookup(i, j, south_boundary)\n",
        "\n",
        "            # create boundary dictionary\n",
        "            boundary = {'loc': np.array([2, 3]),\n",
        "                        'type': np.array([b2_type, b3_type]),\n",
        "                        'value': np.array([b2_value, b3_value])}\n",
        "\n",
        "            # create non-boundary dictionary\n",
        "            non_boundary = {'loc': np.array([1, 4])}\n",
        "\n",
        "            # create well dictionary\n",
        "            well = {'condition': well_condition[i, j],\n",
        "                    'value': well_value[i, j],\n",
        "                    'rw': well_rw[i, j],\n",
        "                    'Gw': well_Gw[i, j]}\n",
        "\n",
        "        if bound_loc[i, j] == 24:\n",
        "            \"\"\"\"\"\"\"\"\"\"\"\n",
        "            MODIFY THIS W.R.T. THE GRID BLOCK LOCATION \n",
        "            \"\"\"\"\"\"\"\"\"\"\"\n",
        "\n",
        "            # get the TYPE and VALUE of the boundary from the BOUNDARY DICTIONARY\n",
        "            b2_type, b2_value = lookup(i, j, east_boundary)\n",
        "            b4_type, b4_value = lookup(i, j, north_boundary)\n",
        "\n",
        "            # create boundary dictionary\n",
        "            boundary = {'loc': np.array([2, 4]),\n",
        "                        'type': np.array([b2_type, b4_type]),\n",
        "                        'value': np.array([b2_value, b4_value])}\n",
        "\n",
        "            # create non-boundary dictionary\n",
        "            non_boundary = {'loc': np.array([1, 3])}\n",
        "\n",
        "            # create well dictionary\n",
        "            well = {'condition': well_condition[i, j],\n",
        "                    'value': well_value[i, j],\n",
        "                    'rw': well_rw[i, j],\n",
        "                    'Gw': well_Gw[i, j]}\n",
        "\n",
        "        if bound_loc[i, j] == 1:\n",
        "            \"\"\"\"\"\"\"\"\"\"\"\n",
        "            MODIFY THIS W.R.T. THE GRID BLOCK LOCATION \n",
        "            \"\"\"\"\"\"\"\"\"\"\"\n",
        "\n",
        "            # get the TYPE and VALUE of the boundary from the BOUNDARY DICTIONARY\n",
        "            b1_type, b1_value = lookup(i, j, west_boundary)\n",
        "\n",
        "            # create boundary dictionary\n",
        "            boundary = {'loc': np.array([1]),\n",
        "                        'type': np.array([b1_type]),\n",
        "                        'value': np.array([b1_value])}\n",
        "\n",
        "            # create non-boundary dictionary\n",
        "            non_boundary = {'loc': np.array([2, 3, 4])}\n",
        "\n",
        "            # create well dictionary\n",
        "            well = {'condition': well_condition[i, j],\n",
        "                    'value': well_value[i, j],\n",
        "                    'rw': well_rw[i, j],\n",
        "                    'Gw': well_Gw[i, j]}\n",
        "\n",
        "        if bound_loc[i, j] == 2:\n",
        "            \"\"\"\"\"\"\"\"\"\"\"\n",
        "            MODIFY THIS W.R.T. THE GRID BLOCK LOCATION \n",
        "            \"\"\"\"\"\"\"\"\"\"\"\n",
        "\n",
        "            # get the TYPE and VALUE of the boundary from the BOUNDARY DICTIONARY\n",
        "            b2_type, b2_value = lookup(i, j, east_boundary)\n",
        "\n",
        "            # create boundary dictionary\n",
        "            boundary = {'loc': np.array([2]),\n",
        "                        'type': np.array([b2_type]),\n",
        "                        'value': np.array([b2_value])}\n",
        "\n",
        "            # create non-boundary dictionary\n",
        "            non_boundary = {'loc': np.array([1, 3, 4])}\n",
        "\n",
        "            # create well dictionary\n",
        "            well = {'condition': well_condition[i, j],\n",
        "                    'value': well_value[i, j],\n",
        "                    'rw': well_rw[i, j],\n",
        "                    'Gw': well_Gw[i, j]}\n",
        "\n",
        "        if bound_loc[i, j] == 3:\n",
        "            \"\"\"\"\"\"\"\"\"\"\"\n",
        "            MODIFY THIS W.R.T. THE GRID BLOCK LOCATION \n",
        "            \"\"\"\"\"\"\"\"\"\"\"\n",
        "\n",
        "            # get the TYPE and VALUE of the boundary from the BOUNDARY DICTIONARY\n",
        "            b3_type, b3_value = lookup(i, j, south_boundary)\n",
        "\n",
        "            # create boundary dictionary\n",
        "            boundary = {'loc': np.array([3]),\n",
        "                        'type': np.array([b3_type]),\n",
        "                        'value': np.array([b3_value])}\n",
        "\n",
        "            # create non-boundary dictionary\n",
        "            non_boundary = {'loc': np.array([1, 2, 4])}\n",
        "\n",
        "            # create well dictionary\n",
        "            well = {'condition': well_condition[i, j],\n",
        "                    'value': well_value[i, j],\n",
        "                    'rw': well_rw[i, j],\n",
        "                    'Gw': well_Gw[i, j]}\n",
        "\n",
        "        if bound_loc[i, j] == 4:\n",
        "            \"\"\"\"\"\"\"\"\"\"\"\n",
        "            MODIFY THIS W.R.T. THE GRID BLOCK LOCATION \n",
        "            \"\"\"\"\"\"\"\"\"\"\"\n",
        "\n",
        "            # get the TYPE and VALUE of the boundary from the BOUNDARY DICTIONARY\n",
        "            b4_type, b4_value = lookup(i, j, north_boundary)\n",
        "\n",
        "            # create boundary dictionary\n",
        "            boundary = {'loc': np.array([4]),\n",
        "                        'type': np.array([b4_type]),\n",
        "                        'value': np.array([b4_value])}\n",
        "\n",
        "            # create non-boundary dictionary\n",
        "            non_boundary = {'loc': np.array([1, 2, 3])}\n",
        "\n",
        "            # create well dictionary\n",
        "            well = {'condition': well_condition[i, j],\n",
        "                    'value': well_value[i, j],\n",
        "                    'rw': well_rw[i, j],\n",
        "                    'Gw': well_Gw[i, j]}\n",
        "\n",
        "        if bound_loc[i, j] == 0:\n",
        "            \"\"\"\"\"\"\"\"\"\"\"\n",
        "            MODIFY THIS W.R.T. THE GRID BLOCK LOCATION \n",
        "            \"\"\"\"\"\"\"\"\"\"\"\n",
        "            # create boundary dictionary\n",
        "            # no boundary\n",
        "            boundary = {'loc': None,\n",
        "                        'type': None,\n",
        "                        'value': None}\n",
        "\n",
        "            # create non-boundary dictionary\n",
        "            non_boundary = {'loc': np.array([1, 2, 3, 4])}\n",
        "\n",
        "            # create well dictionary\n",
        "            well = {'condition': well_condition[i, j],\n",
        "                    'value': well_value[i, j],\n",
        "                    'rw': well_rw[i, j],\n",
        "                    'Gw': well_Gw[i, j]} \n",
        "\n",
        "        \"\"\"\"\"\"\"\"\"\"\"\n",
        "        EXTEND TO 123, 124, 134, 234, 12, 34 BOUNDARIES\n",
        "        \"\"\"\"\"\"\"\"\"\"\"        \n",
        "\n",
        "        if bound_loc[i, j] == 123:\n",
        "            \"\"\"\"\"\"\"\"\"\"\"\n",
        "            MODIFY THIS W.R.T. THE GRID BLOCK LOCATION \n",
        "            \"\"\"\"\"\"\"\"\"\"\"\n",
        "\n",
        "            # get the TYPE and VALUE of the boundary from the BOUNDARY DICTIONARY\n",
        "            b1_type, b1_value = lookup(i, j, west_boundary)\n",
        "            b2_type, b2_value = lookup(i, j, east_boundary)            \n",
        "            b3_type, b3_value = lookup(i, j, south_boundary)\n",
        "\n",
        "            # create boundary dictionary\n",
        "            boundary = {'loc': np.array([1, 2, 3]),\n",
        "                        'type': np.array([b1_type, b2_type, b3_type]),\n",
        "                        'value': np.array([b1_value, b2_value, b3_value])}\n",
        "\n",
        "            # create non-boundary dictionary\n",
        "            non_boundary = {'loc': np.array([4])}\n",
        "\n",
        "            # create well dictionary\n",
        "            well = {'condition': well_condition[i, j],\n",
        "                    'value': well_value[i, j],\n",
        "                    'rw': well_rw[i, j],\n",
        "                    'Gw': well_Gw[i, j]}    \n",
        "\n",
        "        if bound_loc[i, j] == 124:\n",
        "            \"\"\"\"\"\"\"\"\"\"\"\n",
        "            MODIFY THIS W.R.T. THE GRID BLOCK LOCATION \n",
        "            \"\"\"\"\"\"\"\"\"\"\"\n",
        "\n",
        "            # get the TYPE and VALUE of the boundary from the BOUNDARY DICTIONARY\n",
        "            b1_type, b1_value = lookup(i, j, west_boundary)\n",
        "            b2_type, b2_value = lookup(i, j, east_boundary)            \n",
        "            b4_type, b4_value = lookup(i, j, north_boundary)\n",
        "\n",
        "            # create boundary dictionary\n",
        "            boundary = {'loc': np.array([1, 2, 4]),\n",
        "                        'type': np.array([b1_type, b2_type, b4_type]),\n",
        "                        'value': np.array([b1_value, b2_value, b4_value])}\n",
        "\n",
        "            # create non-boundary dictionary\n",
        "            non_boundary = {'loc': np.array([3])}\n",
        "\n",
        "            # create well dictionary\n",
        "            well = {'condition': well_condition[i, j],\n",
        "                    'value': well_value[i, j],\n",
        "                    'rw': well_rw[i, j],\n",
        "                    'Gw': well_Gw[i, j]}   \n",
        "\n",
        "        if bound_loc[i, j] == 134:\n",
        "            \"\"\"\"\"\"\"\"\"\"\"\n",
        "            MODIFY THIS W.R.T. THE GRID BLOCK LOCATION \n",
        "            \"\"\"\"\"\"\"\"\"\"\"\n",
        "\n",
        "            # get the TYPE and VALUE of the boundary from the BOUNDARY DICTIONARY\n",
        "            b1_type, b1_value = lookup(i, j, west_boundary)\n",
        "            b3_type, b3_value = lookup(i, j, south_boundary)            \n",
        "            b4_type, b4_value = lookup(i, j, north_boundary)\n",
        "\n",
        "            # create boundary dictionary\n",
        "            boundary = {'loc': np.array([1, 3, 4]),\n",
        "                        'type': np.array([b1_type, b3_type, b4_type]),\n",
        "                        'value': np.array([b1_value, b3_value, b4_value])}\n",
        "\n",
        "            # create non-boundary dictionary\n",
        "            non_boundary = {'loc': np.array([2])}\n",
        "\n",
        "            # create well dictionary\n",
        "            well = {'condition': well_condition[i, j],\n",
        "                    'value': well_value[i, j],\n",
        "                    'rw': well_rw[i, j],\n",
        "                    'Gw': well_Gw[i, j]}   \n",
        "\n",
        "        if bound_loc[i, j] == 234:\n",
        "            \"\"\"\"\"\"\"\"\"\"\"\n",
        "            MODIFY THIS W.R.T. THE GRID BLOCK LOCATION \n",
        "            \"\"\"\"\"\"\"\"\"\"\"\n",
        "\n",
        "            # get the TYPE and VALUE of the boundary from the BOUNDARY DICTIONARY\n",
        "            b2_type, b2_value = lookup(i, j, east_boundary)\n",
        "            b3_type, b3_value = lookup(i, j, south_boundary)            \n",
        "            b4_type, b4_value = lookup(i, j, north_boundary)\n",
        "\n",
        "            # create boundary dictionary\n",
        "            boundary = {'loc': np.array([2, 3, 4]),\n",
        "                        'type': np.array([b2_type, b3_type, b4_type]),\n",
        "                        'value': np.array([b2_value, b3_value, b4_value])}\n",
        "\n",
        "            # create non-boundary dictionary\n",
        "            non_boundary = {'loc': np.array([1])}\n",
        "\n",
        "            # create well dictionary\n",
        "            well = {'condition': well_condition[i, j],\n",
        "                    'value': well_value[i, j],\n",
        "                    'rw': well_rw[i, j],\n",
        "                    'Gw': well_Gw[i, j]}  \n",
        "\n",
        "        if bound_loc[i, j] == 12:\n",
        "            \"\"\"\"\"\"\"\"\"\"\"\n",
        "            MODIFY THIS W.R.T. THE GRID BLOCK LOCATION \n",
        "            \"\"\"\"\"\"\"\"\"\"\"\n",
        "\n",
        "            # get the TYPE and VALUE of the boundary from the BOUNDARY DICTIONARY\n",
        "            b1_type, b1_value = lookup(i, j, west_boundary)            \n",
        "            b2_type, b2_value = lookup(i, j, east_boundary)\n",
        "\n",
        "            # create boundary dictionary\n",
        "            boundary = {'loc': np.array([1, 2]),\n",
        "                        'type': np.array([b1_type, b2_type]),\n",
        "                        'value': np.array([b1_value, b2_value])}\n",
        "\n",
        "            # create non-boundary dictionary\n",
        "            non_boundary = {'loc': np.array([3, 4])}\n",
        "\n",
        "            # create well dictionary\n",
        "            well = {'condition': well_condition[i, j],\n",
        "                    'value': well_value[i, j],\n",
        "                    'rw': well_rw[i, j],\n",
        "                    'Gw': well_Gw[i, j]}   \n",
        "\n",
        "        if bound_loc[i, j] == 34:\n",
        "            \"\"\"\"\"\"\"\"\"\"\"\n",
        "            MODIFY THIS W.R.T. THE GRID BLOCK LOCATION \n",
        "            \"\"\"\"\"\"\"\"\"\"\"\n",
        "\n",
        "            # get the TYPE and VALUE of the boundary from the BOUNDARY DICTIONARY\n",
        "            b3_type, b3_value = lookup(i, j, south_boundary)            \n",
        "            b4_type, b4_value = lookup(i, j, north_boundary)\n",
        "\n",
        "            # create boundary dictionary\n",
        "            boundary = {'loc': np.array([3, 4]),\n",
        "                        'type': np.array([b3_type, b4_type]),\n",
        "                        'value': np.array([b3_value, b4_value])}\n",
        "\n",
        "            # create non-boundary dictionary\n",
        "            non_boundary = {'loc': np.array([1, 2])}\n",
        "\n",
        "            # create well dictionary\n",
        "            well = {'condition': well_condition[i, j],\n",
        "                    'value': well_value[i, j],\n",
        "                    'rw': well_rw[i, j],\n",
        "                    'Gw': well_Gw[i, j]} \n",
        "\n",
        "        \"\"\"\"\"\"\"\"\"\"\"\n",
        "        DO NOT MODIFY THIS\n",
        "        \"\"\"\"\"\"\"\"\"\"\"\n",
        "\n",
        "        # calculate INTER-BLOCK transmissibilities\n",
        "        T = transmissibility2d(dx, dy, dz, kx, ky, mu, B)\n",
        "        T = np.array(T)\n",
        "\n",
        "        # calculate BOUNDARY transmissibilities\n",
        "\n",
        "        if bound_loc[i, j] == 0.0:\n",
        "            # Interior blocks. All side are non-boundary, so flow are all inter-block\n",
        "            T = T\n",
        "            boundary['T'] = None\n",
        "\n",
        "        else:\n",
        "            # Boundary blocks.\n",
        "            bound_T = []\n",
        "            for k in range(len(boundary['loc'])):\n",
        "                a = boundary['loc'][k]\n",
        "                _ = transmissibility2d_boundary(boundary['loc'][k], boundary['type'][k],\n",
        "                                                dx, dy, dz, kx, ky, mu, B)\n",
        "                T[a - 1] = _\n",
        "                bound_T.append(_)\n",
        "\n",
        "            boundary['T'] = bound_T\n",
        "\n",
        "        # calculate LHS coefficients\n",
        "        px_min, px_plus, py_min, py_plus, p = lhs_coeffs2d_welltype(bound_loc[i, j],\n",
        "                                                                    well, T, mu, B, \n",
        "                                                                    solver='slicomp',\n",
        "                                                                    reservoir_input=reservoir_input,\n",
        "                                                                    timestep=1)\n",
        "        well_dict[i,j] = well\n",
        "        boundary_dict[i,j] = boundary\n",
        "\n",
        "        # fill in LHS matrix\n",
        "        lhs_mat = fill2d_lhs_mat(bound_loc[i, j], block_index[i, j], xi, lhs_mat,\n",
        "                                  px_min, px_plus, py_min, py_plus, p)                      \n",
        "\n",
        "      \"\"\"\"\"\"\"\"\"\n",
        "      INACTIVE BLOCKS\n",
        "      \"\"\"\"\"\"\"\"\"\n",
        "      if bound_loc[i,j] != bound_loc[i,j]:\n",
        "        # skip the loop for INACTIVE BLOCKS\n",
        "        continue \n",
        "\n",
        "\" Timestep evolution of computing RHS and solving the pressure \"\n",
        "\n",
        "# initiate solution pressure with the initial pressure array (p_initial)\n",
        "p_sol = p_initial\n",
        "\n",
        "p_sol_record = []\n",
        "for k in range(schedule):\n",
        "\n",
        "  rhs_mat = np.array([[0.] * 1] * (xi * yi))\n",
        "  for i in range(xi):\n",
        "    for j in range(yi):\n",
        "\n",
        "      if bound_loc[i,j]==bound_loc[i,j]:\n",
        "        \"\"\"\"\"\"\"\"\"\n",
        "        ACTIVE BLOCKS\n",
        "        \"\"\"\"\"\"\"\"\"\n",
        "          \n",
        "        # the pressure in grid block. FIRST timestep, it equals 'p_initial'\n",
        "        # the NEXT timesteps, it equals the solved and updated pressure   \n",
        "        p_sol = p_sol\n",
        "\n",
        "        # calculate potential term \n",
        "        # Equals 0. the grid block is not elevated\n",
        "        potential_term = 0\n",
        "\n",
        "        # calculate RHS constants\n",
        "        rhs = rhs_constant2d_welltype(boundary_dict[i,j], well_dict[i,j], \n",
        "                                      potential_term, dx, dy, dz, kx, ky, mu, B,\n",
        "                                      solver='slicomp', p_initial=p_sol[i,j], \n",
        "                                      reservoir_input=reservoir_input,\n",
        "                                      timestep=timestep)   \n",
        "        # fill in RHS matrix\n",
        "        rhs_mat = fill2d_rhs_mat(block_index[i, j], rhs_mat, rhs)  \n",
        "\n",
        "\n",
        "      \"\"\"\"\"\"\"\"\"\n",
        "      INACTIVE BLOCKS\n",
        "      \"\"\"\"\"\"\"\"\"\n",
        "      if bound_loc[i,j] != bound_loc[i,j]:\n",
        "        # skip the loop for INACTIVE BLOCKS\n",
        "        continue          \n",
        "\n",
        "  \"\"\"\"\"\"\"\"\"\"\"\n",
        "  PRESSURE SOLVER\n",
        "  \"\"\"\"\"\"\"\"\"\"\"  \n",
        "  p_sol = solve_pressure_irregular2d(lhs_mat, rhs_mat, x)\n",
        "  p_sol_record.append(p_sol)   \n",
        "\n",
        "\"\"\"\"\"\"\"\"\"\"\"\n",
        "OUTPUT\n",
        "\"\"\"\"\"\"\"\"\"\"\" \n",
        "\n",
        "p_initial = p_initial.reshape((1,xi,yi))\n",
        "p_sol = np.concatenate((p_initial, p_sol_record), axis=0)"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t6qFaLDI99Z3",
        "colab_type": "text"
      },
      "source": [
        "### Display pressure distribution at certain day"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IOQ_q_23P6Is",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "outputId": "70feafb0-592f-40a1-b4d7-9930dff8c2c7"
      },
      "source": [
        "\"\"\"\n",
        "PRESSURE AT CERTAIN DAY\n",
        "\"\"\"\n",
        "day = 25\n",
        "\n",
        "min, max = np.min(p_sol[~np.isnan(p_sol)]), np.max(p_sol[~np.isnan(p_sol)]) \n",
        "\n",
        "fig, ax = plt.subplots(figsize=(10,6))  \n",
        "im = ax.imshow(p_sol[day].T, cmap='rainbow', vmin=min, vmax=max)\n",
        "ax.set_title('Pressure at day {}'.format(day))\n",
        "cax = fig.add_axes([0.1, 0, 0.8, 0.05])\n",
        "fig.colorbar(im, cax=cax, orientation='horizontal')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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le+2dOwLcLM5oAQAMomgBAAyiaAEADKJoAQAMomgBAAyiaAEADKJoAQAMomgBAAyiaAEADKJoAQAMomgBAAyiaAEADKJoAQAMomgBAAyyfe4AR7qquiTJZ5Jcn2RPd++cNxEAsCyK1nL8x+6+cu4QAMByuXQIADCIojVeJ3lnVZ1TVWesXVlVZ1TVrqratXv37hniAQCjKFrjfVN3PzjJo5I8raoevnJld5/Z3Tu7e+eOHTvmSQgADKFoDdbdl01fr0jyR0lOmTcRALAsitZAVXVcVd1+3/0k357kvHlTAQDL4qcOx/qKJH9UVclirn+vu98+byQAYFkUrYG6++IkD5w7BwAwD5cOAQAGUbQAAAZRtAAABlG0AAAGUbQAAAZRtAAABlG0AAAGUbQAAAZRtAAABlG0AAAGUbQAAAZRtAAABvFLpeFmuOM3/frcEVb5hrkDrPFr171n7gir/MUFJ84dYT9f9ZXXzh1hlbPv9rVzR4AjijNaAACDKFoAAIMoWgAAgyhaAACDKFoAAIMoWgAAgyhaAACDKFoAAIMoWgAAgyhaAACDKFoAAIMoWgAAgyhaAACDKFoAAIMoWoNV1Z2q6qyq+kBVXVhVD507EwCwHNvnDnAr8KIkb+/u762qY5Lcbu5AAMByKFoDVdUdkzw8yZOSpLu/mOSLc2YCAJbHpcOx7plkd5JXVtW5VfWyqjpu5YCqOqOqdlXVrt27d8+TEgAYQtEaa3uSByd5cXc/KMlnkzxn5YDuPrO7d3b3zh07dsyREQAYRNEa69Ikl3b32dPjs7IoXgDArYCiNVB3fyLJx6vqvtOiRya5YMZIAMASeTP8eD+e5LXTTxxenOTJM+cBAJZE0Rqsu9+XZOfcOQCA5XPpEABgEEULAGAQRQsAYBBFCwBgEEULAGAQRQsAYBBFCwBgEEULAGAQRQsAYBBFCwBgEEULAGAQRQsAYBC/VBoY5rXHPmTuCKvc67aXzB1hP5+77ui5I6xy/w9/eO4Iq5x/r3vNHQFuFme0AAAGUbQAAAZRtAAABlG0AAAGUbQAAAZRtAAABlG0AAAGUbQAAAZRtAAABlG0AAAGUbQAAAZRtAAABlG0AAAGUbQAAAZRtAaqqvtW1ftW3K6pqmfNnQsAWI7tcwc4knX3B5N8fZJU1bYklyX5o1lDAQBL44zW8jwyyYe7+6NzBwEAlkPRWp7Tk7xu7cKqOqOqdlXVrt27d88QCwAYRdFagqo6JsnjkvzB2nXdfWZ37+zunTt27Fh+OABgGEVrOR6V5L3d/cm5gwAAy6NoLcfjs85lQwDgyKZoDVZVxyX5tiRvnDsLALBcPt5hsO7+bJIvmzsHALB8zmgBAAyiaAEADKJoAQAMomgBAAyiaAEADKJoAQAMomgBAAyiaAEADKJoAQAMomgBAAyiaAEADKJoAQAMomgBAAyyfe4AAMtyVM2dYH97rt9aoY6qnjsCHFGc0QIAGETRAgAYRNECABhE0QIAGETRAgAYRNECABhE0QIAGETRAgAYRNECABhE0QIAGETRAgAYRNECABhE0QIAGETRGqyqnl1V51fVeVX1uqq6zdyZAIDlULQGqqoTkzwjyc7ufkCSbUlOnzcVALAsitZ425Pctqq2J7ldkn+ZOQ8AsCSK1kDdfVmSFyT5WJLLk3y6u985byoAYFkUrYGq6s5JTktyzyR3S3JcVT1hzZgzqmpXVe3avXv3HDEBgEEUrbG+NclHunt3d38pyRuTfOPKAd19Znfv7O6dO3bsmCUkADCGojXWx5I8pKpuV1WV5JFJLpw5EwCwJIrWQN19dpKzkrw3yT9lMd9nzhoKAFia7XMHONJ19/OSPG/uHADA8jmjBQAwiKIFADCIogUAMIiiBQAwiKIFADCIogUAMIiiBQAwiKIFADCIogUAMIiiBQAwiKIFADCIogUAMIiiBQAwSHX33BmYVNXuJB89DLs6IcmVh2E/RzJzdGDm5+DM0YGZn4M7UufoHt29Y+4QW4WidQSqql3dvXPuHFuZOTow83Nw5ujAzM/BmaNbB5cOAQAGUbQAAAZRtI5MZ84d4BbAHB2Y+Tk4c3Rg5ufgzNGtgPdoAQAM4owWAMAgitYRpqpOraoPVtVFVfWcufNsJVV196r686q6oKrOr6pnzp1pq6qqbVV1blW9Ze4sW01V3amqzqqqD1TVhVX10LkzbTVV9ezp79h5VfW6qrrN3JnmVlWvqKorquq8FcvuUlXvqqoPTV/vPGdGxlC0jiBVtS3JbyZ5VJL7JXl8Vd1v3lRbyp4kP9Hd90vykCRPMz8bemaSC+cOsUW9KMnbu/trkzww5mmVqjoxyTOS7OzuByTZluT0eVNtCa9KcuqaZc9J8qfdfZ8kfzo95gijaB1ZTklyUXdf3N1fTPL6JKfNnGnL6O7Lu/u90/3PZPEP5Inzptp6quqkJI9J8rK5s2w1VXXHJA9P8vIk6e4vdvfV86bakrYnuW1VbU9yuyT/MnOe2XX3u5NctWbxaUlePd1/dZLvWmoolkLROrKcmOTjKx5fGkViXVV1cpIHJTl73iRb0guT/GSSvXMH2YLumWR3kldOl1ZfVlXHzR1qK+nuy5K8IMnHklye5NPd/c55U21ZX9Hdl0/3P5HkK+YMwxiKFrc6VXV8kj9M8qzuvmbuPFtJVT02yRXdfc7cWbao7UkenOTF3f2gJJ+Nyz2rTO8zOi2LUnq3JMdV1RPmTbX19eIjAHwMwBFI0TqyXJbk7isenzQtY1JVR2dRsl7b3W+cO88W9LAkj6uqS7K49PyIqnrNvJG2lEuTXNrd+86EnpVF8eJG35rkI929u7u/lOSNSb5x5kxb1Ser6q5JMn29YuY8DKBoHVn+Icl9quqeVXVMFm9AffPMmbaMqqos3ltzYXf/ytx5tqLufm53n9TdJ2fx+vmz7nY2YtLdn0jy8aq677TokUkumDHSVvSxJA+pqttNf+ceGT8wsJE3J/nB6f4PJvnjGbMwyPa5A3D4dPeeqnp6kndk8ZM+r+ju82eOtZU8LMkTk/xTVb1vWvbT3f3WGTNxy/PjSV47/Wfm4iRPnjnPltLdZ1fVWUnem8VP+p4bn4Ceqnpdkm9JckJVXZrkeUl+KckbquopST6a5PvmS8goPhkeAGAQlw4BAAZRtAAABlG0AAAGUbQAAAZRtAAABlG0AAAGUbQAAAZRtAAABlG0AAAGUbQAAAZRtAAABlG0AAAGUbQAAAZRtAAABlG0AAAGUbQAAAbZfrh3eO86tT+XK29cUIsvvfZxrbPxAdbtt+xQxh7Cfm7S2JXH3/fM1tnPfs85+4/ZcH5y8DHrZqtef/mBlm0287T0gPOz4bFuzLfh81lnP7XmTq2coVp/zIG2v/HhTd1PbzTkAMda+WDj7W8YcqCsa3a6335WzPMhTO8hPa/9t98/z/5/TvuP3Wjf6z2/A+foNY9v7thDee5r9rPuH8lNONb+L8Osnd9199MHeB32Td++1hmz37pVizfYTx/gz7T3u7PufB58P+sce83wQ9l+o20PPqbX326dY+73nDd9rEMYs+G6XmfZIYzZTObNrDvQ/BxS5puZZ79j3sw8Sc5J3tHdp65dftiL1udyZX54264bHu/dtubr9tWP1x2z7UBj+mbupzd/rKFjFuuun5b1UauXrxx/w5h9+1txPvLGMVMJ2neso9bbT298rKPWHOuo1dlX7nP/Mevs56gD5LnhWPvyLL4eteJ5HTUt2zYtq229avnKdfuW1YH2s69IrNlmsZ/V+963fa347n9jnmnMvkKxcj+1Zj9rjrly2ba1Y2qdPJUNx9ywnzXHXLWffeuy8bH2HeOGsTcUrnX2s2Z/q/aTNXnWGbMta/ezd79jbbifrLOfDb6uHL/vGDfst/c/1n5fe+/++5m227bm8XrbHbXOmH3/4N+Qp9fJ02v20+s8rzVjtu1dJ8/a7XudPHvXH7Nt794V+5nW7V2Ta+86eabttq15vHr71etW7WfNsn1ft/U6+9lg7Hr72Xb9/sfa7/j7sl6/4l/QjdateF43jJmOsd/jwzlm7fGvv5ljDnisDbZfOT83a8wB8hxK5nXHrFm35zDl2XMIeZJUckLW4dIhAMAgihYAwCCKFgDAIIoWAMAgihYAwCCKFgDAIIoWAMAgihYAwCCKFgDAIIoWAMAgihYAwCCKFgDAIIoWAMAgihYAwCCKFgDAIIoWAMAgihYAwCCKFgDAIIoWAMAgihYAwCDV3Yd3h1XnJfnCYd0pB3NCkivnDnErZN6Xz5zPw7wvnzmfx82Z9yu7+9S1C7ffvDzr+kJ37xywXzZQVbvM+fKZ9+Uz5/Mw78tnzucxYt5dOgQAGETRAgAYZETROnPAPjkwcz4P87585nwe5n35zPk8Dvu8H/Y3wwMAsODSIQDAIIdctKpqW1WdW1VvmR7fs6rOrqqLqur3q+qYafmx0+OLpvUnr9jHc6flH6yq7zjcT+ZIs4k5f1JV7a6q9023p67Yxw9W1Yem2w/O9VxuSdaZ96dPc95VdcKKcVVVvzat+8eqevCKdeZ9EzYx599SVZ9e8Vr/mRXrTp2+t1xUVc+Z43nckqwz56+d5u+8qnpFVR09Lfc6P4w2Me9e64fJOnP+8qp6//R6Pquqjp+WD+kvmzmj9cwkF654/MtJfrW7753kU0meMi1/SpJPTct/dRqXqrpfktOT3D/JqUl+q6q2bSbsrdChznmS/H53f/10e1mSVNVdkjwvyTckOSXJ86rqzsuJfou2dt7/Jsm3JvnomnGPSnKf6XZGkhcn5v0mOtQ5T5K/WvFa/9lk8Y00yW9m8WdyvySPn77nsLG1c/7aJF+b5N8muW2Sff9h8zo/vA513hOv9cNl7Zw/u7sf2N1fl+RjSZ4+LR/SXw6paFXVSUkek2TfP+CV5BFJzpqGvDrJd033T5seZ1r/yGn8aUle393XdfdHklyUxV9O1rHJOd/IdyR5V3df1d2fSvKuLF4kbGDtvCdJd5/b3ZesM/y0JL/TC+9JcqequmvM+6Zscs43ckqSi7r74u7+YpLXZ/Hnwzo2mPO3Tq/lTvL3SU6aVnmdHyabnPeNeK1vwgZzfs20rrIot/verD6kvxzqGa0XJvnJJHunx1+W5Oru3jM9vjTJidP9E5N8fHoye5J8ehp/w/J1tmF/m5nzJPmeFadB7z4tM+ebt3beD2Sj+TXvm7OZOU+Sh06n/d9WVfeflpnzzdlwzqdLV09M8vZpkdf54bOZeU+81g+Hdee8ql6Z5BNZnE389WnxkP5y0KJVVY9NckV3n3OoO+XmuQlz/n+TnDydBn1XbmzkbILX+vLdhDl/b5J7dPcDs/jm+KZh4Y5QhzDnv5Xk3d39V0uMdcS7CfPutX4zHWjOu/vJSe6WxSXF7x+Z41DOaD0syeOq6pIsTlE+IsmLsjh9vO9X+JyU5LLp/mVJ7p4k0/o7JvnXlcvX2YbVNjXn3f2v3X3dtPxlSf7ddN+cb85+815VrznA+I3m17wfuk3NeXdf093XTvffmuTo6c3y5vzQbTjnVfW8JDuS/LcV473OD49NzbvX+mFxwO8v3X39tPx7pkVj+kt3H/Itybckect0/w+SnD7d/+0kPzbdf1qS357un57kDdP9+yd5f5Jjk9wzycVJtm3m+LfG2yHO+V1XjP/uJO+Z7t8lyUeS3Hm6fSTJXeZ+TreE28p5X7HskiQnrHj8mCRvS1JJHpLk78378Dn/ytz4+X+nZPFG1sri97ZePH1vOWb6XnP/uZ/TVr+t+f7y1CR/m+S2a8Z4nc8z717rA+Z8msN7T8sqyQuSvGB6PKS/3JxfKv1TSV5fVT+f5NwkL5+WvzzJ71bVRUmumsKmu8+vqjckuSDJniRP60Wb5NBtNOfPqKrHZTGvVyV5UpJ091VV9XNJ/mEa97PdfdVyI9/yVdUzsrjG/5VJ/rGq3trdT03y1iSPzuKNkZ9L8uTEvB8OB5jz703yo1W1J8nns/iPRyfZU1VPT/KOJNuSvKK7z58p/i3Vb2fxU55/t3j/b97Yi5908zofa6N591ofo5K8uqruMN1/f5IfndYN6S8+GR4AYBCfDA8AMIiiBQAwiKIFADCIogUAMIiiBV3FMtUAAAAYSURBVAAwiKIFADCIogUAMIiiBQAwyP8HlDrbQSy/iT8AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U1fB4DdnSa2S",
        "colab_type": "text"
      },
      "source": [
        "### Animate pressure change over 50 days"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J0ooyYn2Pu0Z",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490,
          "referenced_widgets": [
            "606c22f2d3264b40836d18db15be2b6c",
            "708e9b9e80fc4b58b29e715fffd2887a",
            "3912b86feac6492bae8130652812b2bf",
            "743dbf147121449990c033a8b12aea12",
            "040b6a2ec3c942629d5f7184cd5fe4fc",
            "92057a34c4fe480f917346717500f2e7",
            "7b06c848b7084af79a03d179b474fabd"
          ]
        },
        "outputId": "f49eeb3e-a590-420a-d89e-44587395b89d"
      },
      "source": [
        "\"\"\"\"\"\"\"\"\"\"\"\n",
        "INTERACTIVE ANIMATION (EVOLUTION OF PRESSURE)\n",
        "\"\"\"\"\"\"\"\"\"\"\"\n",
        "\n",
        "from __future__ import print_function\n",
        "from ipywidgets import interact, interactive, fixed, interact_manual, ToggleButtons\n",
        "import ipywidgets as widgets\n",
        "\n",
        "# min, max = np.round(np.amin(p_sol)), np.round(np.amax(p_sol))\n",
        "\n",
        "@interact\n",
        "\n",
        "def display_pressure(day=(0, schedule)):\n",
        "  fig, ax = plt.subplots(figsize=(10,6))  \n",
        "  im = ax.imshow(p_sol[day].T, cmap='rainbow', vmin=min, vmax=max)\n",
        "  ax.set_title('Simulated reservoir pressure at day {}'.format(day), size=20, pad=15)\n",
        "  cax = fig.add_axes([0.1, 0, 0.8, 0.05])\n",
        "  fig.colorbar(im, cax=cax, orientation='horizontal')\n",
        "\n",
        "  plt.show() "
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "606c22f2d3264b40836d18db15be2b6c",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "interactive(children=(IntSlider(value=25, description='day', max=50), Output()), _dom_classes=('widget-interac…"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s3WxjeYcSeBz",
        "colab_type": "text"
      },
      "source": [
        "### Simulate well and Produce well report"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6_x5EZWagv5I",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\"\"\"\"\"\"\"\"\"\"\"\n",
        "WELL SIMULATION\n",
        "\"\"\"\"\"\"\"\"\"\"\"\n",
        "\n",
        "# create well dataframe\n",
        "B = np.full(len(well_name), reservoir_input['B'])\n",
        "mu = np.full(len(well_name), reservoir_input['mu'])\n",
        "well_df = pd.DataFrame.from_dict(wells)\n",
        "well_df['well_mu'], well_df['well_B'], well_df['well_Gw'] = mu, B, Gw\n",
        "\n",
        "\n",
        "# input the name of well of interest\n",
        "well_name1 = 'W-3'\n",
        "well_report1 = well2d_report_slicomp(well_name1, well_df, p_sol, schedule=schedule)\n",
        "\n",
        "well_name2 = 'W-1'\n",
        "well_report2 = well2d_report_slicomp(well_name2, well_df, p_sol, schedule=schedule)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m_G9iBBmlNIE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "outputId": "4d46f3a6-70d0-43eb-b65d-02a71b767072"
      },
      "source": [
        "plt.figure(figsize=(10,5))\n",
        "\n",
        "plt.plot(well_report1['time (day)'], well_report1['grid block pressure (psi)'], label='Grid Block Pressure (psi)')\n",
        "plt.plot(well_report1['time (day)'], well_report1['fbhp_sol (psi)'], label='Flowing Borehole Pressure (psi)')\n",
        "plt.xlabel('Days'); plt.ylabel('Pressure (psi)')\n",
        "plt.xlim(0, schedule)\n",
        "plt.title('Simulated Flowing Borehole Pressure of Injector Well W-3', size=20, pad=15)\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8NKUjxc8lZxz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        },
        "outputId": "99c227fc-800a-4911-d32b-f74a6f649a77"
      },
      "source": [
        "# well report of injector well W-3 the first 10 days\n",
        "well_report1.head(10)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>time (day)</th>\n",
              "      <th>grid block pressure (psi)</th>\n",
              "      <th>fbhp_sol (psi)</th>\n",
              "      <th>rate_sol (STB/D)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>4000.000000</td>\n",
              "      <td>4115.701231</td>\n",
              "      <td>2600.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>4062.302372</td>\n",
              "      <td>4178.003603</td>\n",
              "      <td>2600.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>4085.506807</td>\n",
              "      <td>4201.208039</td>\n",
              "      <td>2600.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>4103.451531</td>\n",
              "      <td>4219.152762</td>\n",
              "      <td>2600.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>4119.128791</td>\n",
              "      <td>4234.830022</td>\n",
              "      <td>2600.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>5</td>\n",
              "      <td>4133.228339</td>\n",
              "      <td>4248.929570</td>\n",
              "      <td>2600.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>6</td>\n",
              "      <td>4146.061324</td>\n",
              "      <td>4261.762555</td>\n",
              "      <td>2600.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>7</td>\n",
              "      <td>4157.821565</td>\n",
              "      <td>4273.522796</td>\n",
              "      <td>2600.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>8</td>\n",
              "      <td>4168.648789</td>\n",
              "      <td>4284.350020</td>\n",
              "      <td>2600.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>9</td>\n",
              "      <td>4178.651017</td>\n",
              "      <td>4294.352249</td>\n",
              "      <td>2600.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   time (day)  grid block pressure (psi)  fbhp_sol (psi)  rate_sol (STB/D)\n",
              "0           0                4000.000000     4115.701231            2600.0\n",
              "1           1                4062.302372     4178.003603            2600.0\n",
              "2           2                4085.506807     4201.208039            2600.0\n",
              "3           3                4103.451531     4219.152762            2600.0\n",
              "4           4                4119.128791     4234.830022            2600.0\n",
              "5           5                4133.228339     4248.929570            2600.0\n",
              "6           6                4146.061324     4261.762555            2600.0\n",
              "7           7                4157.821565     4273.522796            2600.0\n",
              "8           8                4168.648789     4284.350020            2600.0\n",
              "9           9                4178.651017     4294.352249            2600.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7eGL4cXwlj3R",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "outputId": "89385767-6550-46a3-b66e-c6ae83e0f735"
      },
      "source": [
        "plt.figure(figsize=(10,5))\n",
        "\n",
        "plt.plot(well_report2['time (day)'], np.abs(well_report2['rate_sol (STB/D)']), color='red', label='Flow Rate (STB/D)')\n",
        "plt.xlabel('Days'); plt.ylabel('Rate (STB/D)')\n",
        "plt.title('Simulated Flow Rate of Producer Well W-1', size=20, pad=15)\n",
        "plt.xlim(0, schedule)\n",
        "plt.ylim(ymin=0)\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GXGNE8aY8IKJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        },
        "outputId": "707f892a-9567-40a7-a242-a5416a54ece8"
      },
      "source": [
        "# well report of producer well W-3 the first 10 days\n",
        "well_report2.head(10)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>time (day)</th>\n",
              "      <th>grid block pressure (psi)</th>\n",
              "      <th>fbhp_sol (psi)</th>\n",
              "      <th>rate_sol (STB/D)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>4000.000000</td>\n",
              "      <td>4000.0</td>\n",
              "      <td>-0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>4004.380610</td>\n",
              "      <td>4000.0</td>\n",
              "      <td>-98.439622</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>4010.109648</td>\n",
              "      <td>4000.0</td>\n",
              "      <td>-227.180679</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>4016.037169</td>\n",
              "      <td>4000.0</td>\n",
              "      <td>-360.381977</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>4021.870998</td>\n",
              "      <td>4000.0</td>\n",
              "      <td>-491.477875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>5</td>\n",
              "      <td>4027.520009</td>\n",
              "      <td>4000.0</td>\n",
              "      <td>-618.420598</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>6</td>\n",
              "      <td>4032.941011</td>\n",
              "      <td>4000.0</td>\n",
              "      <td>-740.239564</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>7</td>\n",
              "      <td>4038.109834</td>\n",
              "      <td>4000.0</td>\n",
              "      <td>-856.391650</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>8</td>\n",
              "      <td>4043.014321</td>\n",
              "      <td>4000.0</td>\n",
              "      <td>-966.603675</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>9</td>\n",
              "      <td>4047.650856</td>\n",
              "      <td>4000.0</td>\n",
              "      <td>-1070.794364</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   time (day)  grid block pressure (psi)  fbhp_sol (psi)  rate_sol (STB/D)\n",
              "0           0                4000.000000          4000.0         -0.000000\n",
              "1           1                4004.380610          4000.0        -98.439622\n",
              "2           2                4010.109648          4000.0       -227.180679\n",
              "3           3                4016.037169          4000.0       -360.381977\n",
              "4           4                4021.870998          4000.0       -491.477875\n",
              "5           5                4027.520009          4000.0       -618.420598\n",
              "6           6                4032.941011          4000.0       -740.239564\n",
              "7           7                4038.109834          4000.0       -856.391650\n",
              "8           8                4043.014321          4000.0       -966.603675\n",
              "9           9                4047.650856          4000.0      -1070.794364"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    }
  ]
}