OIL_FIELD_PART_3.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "OIL FIELD PART 3.ipynb",
      "provenance": [],
      "mount_file_id": "1rTQFVdvphlPInqK8F27fbnxx6bDkAeXK",
      "authorship_tag": "ABX9TyPgEuVlrpU1MHWb0/oxMwyX",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/bibhabasumohapatra/Decline-Curve-Analysis/blob/main/OIL_FIELD_PART_3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TtlEdBFhV0gb"
      },
      "source": [
        "import torch \n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_YuQY1J7VjOp"
      },
      "source": [
        "# Creating Data\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1XnfOJPCVV0Q"
      },
      "source": [
        "path = '/content/drive/MyDrive/Prodn_03-2021 - Prod.csv'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SpMrX7f9Wg2X"
      },
      "source": [
        "df = pd.read_csv(path)\n",
        "df = df[(df['OILCM'].notnull()) & (df['OILCM']>0)]\n",
        "df = df[(df['LIQUID'].notnull()) & (df['LIQUID']>0)]\n",
        "df = df[(df['WATER'].notnull()) & (df['WATER']>0)]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v2G10LUnWpWK"
      },
      "source": [
        "# df['OILCM'] = df['OILCM']/df['FDAYS']\n",
        "# df['LIQUID'] = df['LIQUID']/df['FDAYS']\n",
        "# df['OILCM'] = df['OILCM']/df['FDAYS']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B_9Kqo-GXlaK"
      },
      "source": [
        "df = df[df['WLNO']=='MAN36']\n",
        "arr = []\n",
        "for i in range(len(df)):\n",
        "  arr.append(i)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e8Cw2krOYLBm"
      },
      "source": [
        "FINAL USED DATA"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8E7p_QGDYJbh"
      },
      "source": [
        "time = arr ##in days\n",
        "OIL = df['OILCM'].to_list()\n",
        "WATER = df['WATER'].to_list()\n",
        "LIQUID = df['LIQUID'].to_list()  "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2lGrcc9PcieL"
      },
      "source": [
        "qi = 624"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zx_qC-DQdVbf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fd672359-b811-49e9-8868-3c75ec1a7afd"
      },
      "source": [
        "OIL = torch.tensor(OIL).unsqueeze(1)\n",
        "WATER = torch.tensor(WATER).unsqueeze(1)\n",
        "LIQUID = torch.tensor(LIQUID).unsqueeze(1)\n",
        "time = torch.tensor(time).unsqueeze(1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  \n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:4: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  after removing the cwd from sys.path.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VhXYOweZdBWv"
      },
      "source": [
        "Main work -- Mechanics of learning starts here"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fu79d8U8Vt8-"
      },
      "source": [
        "def hyperbolic_equation(t, qi, b, di):\n",
        "    return qi/((1.0+b*di*t)**(1.0/b))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W7kl6DccfSAH"
      },
      "source": [
        "def loss_fn(q,q_0):\n",
        "  squared_diff = (q - q_0)**2\n",
        "  return squared_diff.mean()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OZ8dVCeNVzkU"
      },
      "source": [
        "# optimizers \n",
        "import torch.optim as optim"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qp2NkYMhdSZx"
      },
      "source": [
        "params = torch.tensor([qi,1.0,0.0],requires_grad=True)\n",
        "learning_rate = 1e-2\n",
        "optimizer = optim.Adam([params],lr=learning_rate)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 955
        },
        "id": "di7qqMV9eRFO",
        "outputId": "59a0233d-9714-45d2-dffb-255cf20c8480"
      },
      "source": [
        "q = hyperbolic_equation(time,*params)\n",
        "loss = torch.nn.MSELoss(q,OIL)\n",
        "\n",
        "optimizer.zero_grad()\n",
        "loss.backward()\n",
        "optimizer.step()\n",
        "\n",
        "print(params)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "RuntimeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-15-0abbee20f8e1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mq\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhyperbolic_equation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMSELoss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mq\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mOIL\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzero_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/nn/modules/loss.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, size_average, reduce, reduction)\u001b[0m\n\u001b[1;32m    523\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    524\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize_average\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreduce\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreduction\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'mean'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 525\u001b[0;31m         \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mMSELoss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msize_average\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreduce\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreduction\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    526\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    527\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/nn/modules/loss.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, size_average, reduce, reduction)\u001b[0m\n\u001b[1;32m     16\u001b[0m         \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_Loss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0msize_average\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mreduce\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreduction\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_Reduction\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlegacy_get_string\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msize_average\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreduce\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     19\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreduction\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mreduction\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/nn/_reduction.py\u001b[0m in \u001b[0;36mlegacy_get_string\u001b[0;34m(size_average, reduce, emit_warning)\u001b[0m\n\u001b[1;32m     33\u001b[0m         \u001b[0mreduce\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     34\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 35\u001b[0;31m     \u001b[0;32mif\u001b[0m \u001b[0msize_average\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mreduce\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     36\u001b[0m         \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'mean'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     37\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mreduce\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mRuntimeError\u001b[0m: Boolean value of Tensor with more than one value is ambiguous"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q-Q0oqdbgbt4"
      },
      "source": [
        "def training_loop(n_epochs,optimizer,params,t,OIL):\n",
        "  for epochs in range(1,n_epochs+1):\n",
        "    q = hyperbolic_equation(time,*params)\n",
        "    loss = torch(q,OIL)\n",
        "\n",
        "    optimizer.zero_grad()\n",
        "    loss.backward()\n",
        "    optimizer.step()  \n",
        "\n",
        "    if epochs%5000 == 0:\n",
        "       print('Epoch %d, Loss %f'%(epochs,float(loss)))\n",
        "  return params    "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u-7VDw1Vhtzy",
        "outputId": "0e81c903-98f3-4027-ffad-a8ce4c8f2629"
      },
      "source": [
        "params = torch.tensor([qi,1.0,0.0],requires_grad=True)\n",
        "learning_rate = 1e-2\n",
        "optimizer = optim.Adam([params],lr=learning_rate)\n",
        "training_loop(\n",
        "    n_epochs = 50000,\n",
        "    optimizer = optimizer,\n",
        "    params = params,\n",
        "    t = time,\n",
        "    OIL = OIL\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 5000, Loss 6800.770020\n",
            "Epoch 10000, Loss 6792.202637\n",
            "Epoch 15000, Loss 6795.818359\n",
            "Epoch 20000, Loss 6792.515137\n",
            "Epoch 25000, Loss 6796.008301\n",
            "Epoch 30000, Loss 6792.393066\n",
            "Epoch 35000, Loss 6792.771973\n",
            "Epoch 40000, Loss 6792.597656\n",
            "Epoch 45000, Loss 6793.457031\n",
            "Epoch 50000, Loss 6792.667969\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([6.5363e+02, 5.2485e-01, 1.7085e-02], requires_grad=True)"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5QS4vi0yjKQH"
      },
      "source": [
        "final = [6.5363e+02, 5.2485e-01, 1.7085e-02]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ILoP_QI9IpwV"
      },
      "source": [
        "time_temp = []\n",
        "for i in range(200):\n",
        "  time_temp.append(i)\n",
        "time_temp = torch.tensor(time_temp)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 764
        },
        "id": "VcEuuOQ4jPAz",
        "outputId": "f11df5fc-2a11-4b2f-c4ea-5f0142a7ddb5"
      },
      "source": [
        "q_final = hyperbolic_equation(time_temp,*final)\n",
        "fig = plt.figure(dpi=200)\n",
        "plt.xlabel('month')\n",
        "plt.ylabel('OIL/month')\n",
        "plt.plot(time.numpy(),OIL.numpy())\n",
        "plt.plot(time_temp.numpy(),q_final.numpy())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fec0ead1190>]"
            ]
          },
          "metadata": {},
          "execution_count": 73
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7dTxk2qClP-Y"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}