{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "oil_reservoir_drive_notebook.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyPLce2+kiPgiSJH0TVelK53",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/yohanesnuwara/pyreservoir/blob/master/notebooks/oil_reservoir_drive_notebook.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pimZYJ_NnOEH",
        "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": "r6DhGxygxNXx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 138
        },
        "outputId": "730a5bcc-5ea8-4a9b-fb65-c09a51260e5e"
      },
      "source": [
        "!git clone https://github.com/yohanesnuwara/pyreservoir"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'pyreservoir'...\n",
            "remote: Enumerating objects: 139, done.\u001b[K\n",
            "remote: Counting objects: 100% (139/139), done.\u001b[K\n",
            "remote: Compressing objects: 100% (139/139), done.\u001b[K\n",
            "remote: Total 373 (delta 73), reused 0 (delta 0), pack-reused 234\u001b[K\n",
            "Receiving objects: 100% (373/373), 475.72 KiB | 683.00 KiB/s, done.\n",
            "Resolving deltas: 100% (195/195), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iaBVJ1oyjtbJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import sys\n",
        "\n",
        "sys.path.append('/content/pyreservoir/pvt')\n",
        "from pvtlab import linear_interpolate\n",
        "\n",
        "sys.path.append('/content/pyreservoir/matbal')\n",
        "from aquifer import schilthuis, veh, fetkovich\n",
        "\n",
        "sys.path.append('/content/pyreservoir/matbal')\n",
        "from drives import saturated_nonvolatile_totaloil, energy_plot"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LHZh3KHvhkQK",
        "colab_type": "text"
      },
      "source": [
        "### Part 1. Processing Production and PVT Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c9b87GRCWxX7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        },
        "outputId": "0d0d1828-0766-4b00-c3da-f08cc11eabcb"
      },
      "source": [
        "# load production data\n",
        "columns = ['date', 'p', 'Np', 'Wp', 'Wi', 'Wp-Wi', 'Gp']\n",
        "df = pd.read_csv('/content/pyreservoir/data/conroe_proddata.csv', names=columns)\n",
        "\n",
        "# load PVT data\n",
        "columns = ['p', 'Bg', 'Bt']\n",
        "pvt = pd.read_csv('/content/pyreservoir/data/conroe_pvtdata.csv', names=columns)\n",
        "\n",
        "# define variables in PVT data\n",
        "p, Bg, Bt = pvt['p'].values, pvt['Bg'].values, pvt['Bt'].values\n",
        "\n",
        "# define variables in production data\n",
        "p1 = df['p'].values\n",
        "\n",
        "# interpolate Bg and Bt from PVT to production data\n",
        "Bg_interpolated = linear_interpolate(p, p1, Bg)\n",
        "Bt_interpolated = linear_interpolate(p, p1, Bt)\n",
        "\n",
        "# add the interpolated Bg and Bt data into production dataframe\n",
        "df['Bg'] = Bg_interpolated\n",
        "df['Bt'] = Bt_interpolated\n",
        "\n",
        "# convert time column to datetime\n",
        "df['date'] =  pd.to_datetime(df['date'], format='%d %B %Y')\n",
        "\n",
        "# define input variables\n",
        "t = df['date'].values\n",
        "p = df['p'].values\n",
        "Np = df['Np'].values\n",
        "Wp = df['Wp'].values\n",
        "Wi = df['Wi'].values\n",
        "Gp = df['Gp'].values * 1E+3 # convert to SCF  \n",
        "Bg = df['Bg'].values * (1 / 1E+3) # convert to RB/SCF\n",
        "Bt = df['Bt'].values\n",
        "Bw = np.full(len(df), 1) # Water FVF is made constant, 1 RB/STB\n",
        "\n",
        "df.head(10)"
      ],
      "execution_count": 4,
      "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>date</th>\n",
              "      <th>p</th>\n",
              "      <th>Np</th>\n",
              "      <th>Wp</th>\n",
              "      <th>Wi</th>\n",
              "      <th>Wp-Wi</th>\n",
              "      <th>Gp</th>\n",
              "      <th>Bg</th>\n",
              "      <th>Bt</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1932-01-01</td>\n",
              "      <td>2180.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.187000</td>\n",
              "      <td>1.205000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1932-07-01</td>\n",
              "      <td>2177.0</td>\n",
              "      <td>1341096.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3330412.0</td>\n",
              "      <td>1.188875</td>\n",
              "      <td>1.205563</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1933-01-01</td>\n",
              "      <td>2170.0</td>\n",
              "      <td>2681691.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6412824.0</td>\n",
              "      <td>1.193250</td>\n",
              "      <td>1.206875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1933-07-01</td>\n",
              "      <td>2148.0</td>\n",
              "      <td>9784770.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>14900678.0</td>\n",
              "      <td>1.206469</td>\n",
              "      <td>1.211265</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1934-01-01</td>\n",
              "      <td>2125.0</td>\n",
              "      <td>23440301.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>24295374.0</td>\n",
              "      <td>1.220082</td>\n",
              "      <td>1.215959</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1934-07-01</td>\n",
              "      <td>2110.0</td>\n",
              "      <td>32325186.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>30585954.0</td>\n",
              "      <td>1.229000</td>\n",
              "      <td>1.219100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1935-01-01</td>\n",
              "      <td>2103.0</td>\n",
              "      <td>39819896.0</td>\n",
              "      <td>71470.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>71470.0</td>\n",
              "      <td>35940966.0</td>\n",
              "      <td>1.233200</td>\n",
              "      <td>1.220640</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1935-07-01</td>\n",
              "      <td>2105.0</td>\n",
              "      <td>47414017.0</td>\n",
              "      <td>143243.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>143243.0</td>\n",
              "      <td>41313677.0</td>\n",
              "      <td>1.232000</td>\n",
              "      <td>1.220200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1936-01-01</td>\n",
              "      <td>2096.0</td>\n",
              "      <td>54185666.0</td>\n",
              "      <td>188180.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>188180.0</td>\n",
              "      <td>46221132.0</td>\n",
              "      <td>1.237400</td>\n",
              "      <td>1.222180</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1936-07-01</td>\n",
              "      <td>2089.0</td>\n",
              "      <td>61129694.0</td>\n",
              "      <td>346001.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>346001.0</td>\n",
              "      <td>50923634.0</td>\n",
              "      <td>1.241600</td>\n",
              "      <td>1.223720</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        date       p          Np  ...          Gp        Bg        Bt\n",
              "0 1932-01-01  2180.0         0.0  ...         0.0  1.187000  1.205000\n",
              "1 1932-07-01  2177.0   1341096.0  ...   3330412.0  1.188875  1.205563\n",
              "2 1933-01-01  2170.0   2681691.0  ...   6412824.0  1.193250  1.206875\n",
              "3 1933-07-01  2148.0   9784770.0  ...  14900678.0  1.206469  1.211265\n",
              "4 1934-01-01  2125.0  23440301.0  ...  24295374.0  1.220082  1.215959\n",
              "5 1934-07-01  2110.0  32325186.0  ...  30585954.0  1.229000  1.219100\n",
              "6 1935-01-01  2103.0  39819896.0  ...  35940966.0  1.233200  1.220640\n",
              "7 1935-07-01  2105.0  47414017.0  ...  41313677.0  1.232000  1.220200\n",
              "8 1936-01-01  2096.0  54185666.0  ...  46221132.0  1.237400  1.222180\n",
              "9 1936-07-01  2089.0  61129694.0  ...  50923634.0  1.241600  1.223720\n",
              "\n",
              "[10 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xC-oISCqfQdi",
        "colab_type": "text"
      },
      "source": [
        "### Part 2. Calculate Aquifer Influx"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NF-FS8Bbf-1R",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "outputId": "f04bd7b7-6a22-4e81-b3c8-495daafc16f9"
      },
      "source": [
        "# all known data of Conroe field\n",
        "Nfoi = 800.44 * 1E+6 # OOIP from volumetrics, STB\n",
        "Gfgi = 350 * 1E+9 # Original gas cap volume from volumetrics, SCF\n",
        "Rsi = 600 # SCF/STB\n",
        "Boi = 1.205 # Original oil FVF, RB/STB\n",
        "Bgi = 1.187 * (1 / 1E+3) # Original gas FVF, convert to RB/SCF\n",
        "Vwi = 20 * 1E+6 # Interstitial water volume, bbl\n",
        "hsand = 200 # Gross sand thickness, ft\n",
        "poro = 0.28\n",
        "re = 6600 # Reservoir radius, ft\n",
        "r_aq = 264000 # Aquifer radius, ft\n",
        "k = 265 # Permeability, md\n",
        "cw = 3.4E-6 # Water compressibility, sip\n",
        "cf = 3.4E-6 # Formation compressibility, sip\n",
        "mu_w = 0.38 # Water viscosity, cp\n",
        "\n",
        "# calculate Swi from hsand, poro, and Vwi\n",
        "Vbulk = np.pi * (re**2) * hsand # ft3\n",
        "PV = poro * Vbulk # pore volume, ft3\n",
        "Vwi = Vwi * 5.6145 # convert to ft3\n",
        "swi = Vwi / PV\n",
        "\n",
        "# calculate aquifer influx \n",
        "import sys\n",
        "sys.path.append('/content/pyreservoir/matbal')\n",
        "from aquifer import *\n",
        "\n",
        "## Schilthuis\n",
        "method = schilthuis()\n",
        "We_schilthuis = We_schilthuis = schilthuis.calculate_aquifer(method, p, Bw, Wp, Np, Bt, Nfoi, cf, cw, swi, Bt[0])\n",
        "\n",
        "## VEH\n",
        "method = veh()\n",
        "B_star = veh.calculate_aquifer_constant(method, re, hsand, cf, cw, poro)\n",
        "We_veh = veh.calculate_aquifer(method, t, p, cf, cw, k, poro, mu_w, re, B_star)\n",
        "\n",
        "## Fetkovich\n",
        "method = fetkovich()\n",
        "ct = cf + cw\n",
        "theta = 360\n",
        "Wei = fetkovich.initial_encroachable_water(method, p[0], ct, re, r_aq, hsand, poro, theta)\n",
        "J = fetkovich.productivity_index(method, k, hsand, mu_w, r_aq, re, theta, flow='no flow')\n",
        "We_fetkovich = fetkovich.calculate_aquifer(method, t, p, Wei, J)\n",
        "\n",
        "# plot the aquifer influx\n",
        "plt.figure(figsize=(10,7))\n",
        "plt.plot(t, We_schilthuis, '.-', label='Schilthuis')\n",
        "plt.plot(t, We_veh, '.-', label='van Everdingen-Hurst')\n",
        "plt.plot(t, We_fetkovich, '.-', label='Fetkovich (No Terminal Flow)')\n",
        "plt.title('Aquifer Influx of Conroe Field')\n",
        "plt.xlabel('Year')\n",
        "plt.ylabel('Water influx (res bbl)')\n",
        "plt.xlim(min(t), max(t)); plt.ylim(ymin=0)\n",
        "\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7ylbapyJsZNK",
        "colab_type": "text"
      },
      "source": [
        "### Part 3. Calculate Pirson Drive Indices and Display Energy Plot"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lW7ebJqn_DXu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "13abff60-0065-4515-dc5f-cffd5c1b371b"
      },
      "source": [
        "# initialize with type: saturated_nonvolatile_totaloil\n",
        "type = saturated_nonvolatile_totaloil()\n",
        "\n",
        "# calculate material balance parameters\n",
        "F, Efw, Eo, Eg = saturated_nonvolatile_totaloil.calculate_params(type, p, Bg, Bt, Rsi, Np, Gp, cf, cw, swi)\n",
        "\n",
        "# calculate drive indices\n",
        "Idd, Isd, Ifd, Iwd, Iwi = saturated_nonvolatile_totaloil.indices(type, F, Efw, Eo, Eg, Nfoi, Gfgi, Boi, Bgi, We_schilthuis, Bw, Wp, Wi)\n",
        "\n",
        "# display energy plot\n",
        "energy_plot(t, Idd, Isd, Ifd, Iwi)\n"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}