LSTM_for_Production_Univariate.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "b0fc1b9e",
      "metadata": {
        "id": "b0fc1b9e"
      },
      "source": [
        "# LSTM for Production: Univariate"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "cc093637",
      "metadata": {
        "id": "cc093637"
      },
      "source": [
        "In this repo, we will use the production data of the [Equinor’s Volve data set](https://www.equinor.com/energy/volve-data-sharing), a publicly available subsurface data set, to train three separate models to predict oil, gas, and water production respectively."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "RTsvfb4X68F2",
      "metadata": {
        "id": "RTsvfb4X68F2"
      },
      "source": [
        "If you are using Colab to run this notebook, you have to run the below cell. Change the `content/drive/My Drive` according to your directly. If you are running this notebook on a local environment i.e. Anaconda or VS code, ignore the below cell. But make sure your notebook and dataset are in the same directory."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2FTG9_o-TtlX",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2FTG9_o-TtlX",
        "outputId": "35798ecd-3986-4c96-9940-d5355f7f7c96"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "import os\n",
        "os.chdir('/content/drive/My Drive')\n",
        "os.path.exists('/content/drive/My Drive')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9423f78b",
      "metadata": {
        "id": "9423f78b"
      },
      "source": [
        "First, import the libraries and framework to use.  \n",
        "I am using `random seed 42` to reproduce the same random number everytime I run the code."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a82fe03f",
      "metadata": {
        "id": "a82fe03f"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "tf.keras.utils.set_random_seed(42)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "72581fac",
      "metadata": {
        "id": "72581fac"
      },
      "source": [
        "Now import the Excel file named `Volve_production_data` which contains our data.  \n",
        "Take a moment to understand the data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ac412ae9",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "id": "ac412ae9",
        "outputId": "e1fc4525-8d96-4b26-8cb8-f3c6abd9e6b7"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-9e75abd0-5af4-4196-85a1-6495a40ed7e4\" class=\"colab-df-container\">\n",
              "    <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>DATEPRD</th>\n",
              "      <th>WELL_BORE_CODE</th>\n",
              "      <th>NPD_WELL_BORE_CODE</th>\n",
              "      <th>NPD_WELL_BORE_NAME</th>\n",
              "      <th>NPD_FIELD_CODE</th>\n",
              "      <th>NPD_FIELD_NAME</th>\n",
              "      <th>NPD_FACILITY_CODE</th>\n",
              "      <th>NPD_FACILITY_NAME</th>\n",
              "      <th>ON_STREAM_HRS</th>\n",
              "      <th>AVG_DOWNHOLE_PRESSURE</th>\n",
              "      <th>...</th>\n",
              "      <th>AVG_CHOKE_UOM</th>\n",
              "      <th>AVG_WHP_P</th>\n",
              "      <th>AVG_WHT_P</th>\n",
              "      <th>DP_CHOKE_SIZE</th>\n",
              "      <th>BORE_OIL_VOL</th>\n",
              "      <th>BORE_GAS_VOL</th>\n",
              "      <th>BORE_WAT_VOL</th>\n",
              "      <th>BORE_WI_VOL</th>\n",
              "      <th>FLOW_KIND</th>\n",
              "      <th>WELL_TYPE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2014-04-07</td>\n",
              "      <td>NO 15/9-F-1 C</td>\n",
              "      <td>7405</td>\n",
              "      <td>15/9-F-1 C</td>\n",
              "      <td>3420717</td>\n",
              "      <td>VOLVE</td>\n",
              "      <td>369304</td>\n",
              "      <td>MÆRSK INSPIRER</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>...</td>\n",
              "      <td>%</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>production</td>\n",
              "      <td>WI</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2014-04-08</td>\n",
              "      <td>NO 15/9-F-1 C</td>\n",
              "      <td>7405</td>\n",
              "      <td>15/9-F-1 C</td>\n",
              "      <td>3420717</td>\n",
              "      <td>VOLVE</td>\n",
              "      <td>369304</td>\n",
              "      <td>MÆRSK INSPIRER</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>%</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>production</td>\n",
              "      <td>OP</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2014-04-09</td>\n",
              "      <td>NO 15/9-F-1 C</td>\n",
              "      <td>7405</td>\n",
              "      <td>15/9-F-1 C</td>\n",
              "      <td>3420717</td>\n",
              "      <td>VOLVE</td>\n",
              "      <td>369304</td>\n",
              "      <td>MÆRSK INSPIRER</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>%</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>production</td>\n",
              "      <td>OP</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2014-04-10</td>\n",
              "      <td>NO 15/9-F-1 C</td>\n",
              "      <td>7405</td>\n",
              "      <td>15/9-F-1 C</td>\n",
              "      <td>3420717</td>\n",
              "      <td>VOLVE</td>\n",
              "      <td>369304</td>\n",
              "      <td>MÆRSK INSPIRER</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>%</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>production</td>\n",
              "      <td>OP</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2014-04-11</td>\n",
              "      <td>NO 15/9-F-1 C</td>\n",
              "      <td>7405</td>\n",
              "      <td>15/9-F-1 C</td>\n",
              "      <td>3420717</td>\n",
              "      <td>VOLVE</td>\n",
              "      <td>369304</td>\n",
              "      <td>MÆRSK INSPIRER</td>\n",
              "      <td>0.0</td>\n",
              "      <td>310.37614</td>\n",
              "      <td>...</td>\n",
              "      <td>%</td>\n",
              "      <td>33.09788</td>\n",
              "      <td>10.47992</td>\n",
              "      <td>33.07195</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>production</td>\n",
              "      <td>OP</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15629</th>\n",
              "      <td>2016-09-14</td>\n",
              "      <td>NO 15/9-F-5 AH</td>\n",
              "      <td>5769</td>\n",
              "      <td>15/9-F-5</td>\n",
              "      <td>3420717</td>\n",
              "      <td>VOLVE</td>\n",
              "      <td>369304</td>\n",
              "      <td>MÆRSK INSPIRER</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>%</td>\n",
              "      <td>0.07776</td>\n",
              "      <td>0.22879</td>\n",
              "      <td>0.01862</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>production</td>\n",
              "      <td>OP</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15630</th>\n",
              "      <td>2016-09-15</td>\n",
              "      <td>NO 15/9-F-5 AH</td>\n",
              "      <td>5769</td>\n",
              "      <td>15/9-F-5</td>\n",
              "      <td>3420717</td>\n",
              "      <td>VOLVE</td>\n",
              "      <td>369304</td>\n",
              "      <td>MÆRSK INSPIRER</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>%</td>\n",
              "      <td>0.08545</td>\n",
              "      <td>0.22914</td>\n",
              "      <td>0.00631</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>production</td>\n",
              "      <td>OP</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15631</th>\n",
              "      <td>2016-09-16</td>\n",
              "      <td>NO 15/9-F-5 AH</td>\n",
              "      <td>5769</td>\n",
              "      <td>15/9-F-5</td>\n",
              "      <td>3420717</td>\n",
              "      <td>VOLVE</td>\n",
              "      <td>369304</td>\n",
              "      <td>MÆRSK INSPIRER</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>%</td>\n",
              "      <td>0.08544</td>\n",
              "      <td>0.22896</td>\n",
              "      <td>0.01181</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>production</td>\n",
              "      <td>OP</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15632</th>\n",
              "      <td>2016-09-17</td>\n",
              "      <td>NO 15/9-F-5 AH</td>\n",
              "      <td>5769</td>\n",
              "      <td>15/9-F-5</td>\n",
              "      <td>3420717</td>\n",
              "      <td>VOLVE</td>\n",
              "      <td>369304</td>\n",
              "      <td>MÆRSK INSPIRER</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>%</td>\n",
              "      <td>0.07497</td>\n",
              "      <td>0.22846</td>\n",
              "      <td>0.02576</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>production</td>\n",
              "      <td>OP</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15633</th>\n",
              "      <td>2016-09-18</td>\n",
              "      <td>NO 15/9-F-5 AH</td>\n",
              "      <td>5769</td>\n",
              "      <td>15/9-F-5</td>\n",
              "      <td>3420717</td>\n",
              "      <td>VOLVE</td>\n",
              "      <td>369304</td>\n",
              "      <td>MÆRSK INSPIRER</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>injection</td>\n",
              "      <td>WI</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>15634 rows × 24 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9e75abd0-5af4-4196-85a1-6495a40ed7e4')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-9e75abd0-5af4-4196-85a1-6495a40ed7e4 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-9e75abd0-5af4-4196-85a1-6495a40ed7e4');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-17cd50c0-5826-42e3-893f-a5fe1704e4f8\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-17cd50c0-5826-42e3-893f-a5fe1704e4f8')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-17cd50c0-5826-42e3-893f-a5fe1704e4f8 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "         DATEPRD  WELL_BORE_CODE  NPD_WELL_BORE_CODE NPD_WELL_BORE_NAME  \\\n",
              "0     2014-04-07   NO 15/9-F-1 C                7405         15/9-F-1 C   \n",
              "1     2014-04-08   NO 15/9-F-1 C                7405         15/9-F-1 C   \n",
              "2     2014-04-09   NO 15/9-F-1 C                7405         15/9-F-1 C   \n",
              "3     2014-04-10   NO 15/9-F-1 C                7405         15/9-F-1 C   \n",
              "4     2014-04-11   NO 15/9-F-1 C                7405         15/9-F-1 C   \n",
              "...          ...             ...                 ...                ...   \n",
              "15629 2016-09-14  NO 15/9-F-5 AH                5769           15/9-F-5   \n",
              "15630 2016-09-15  NO 15/9-F-5 AH                5769           15/9-F-5   \n",
              "15631 2016-09-16  NO 15/9-F-5 AH                5769           15/9-F-5   \n",
              "15632 2016-09-17  NO 15/9-F-5 AH                5769           15/9-F-5   \n",
              "15633 2016-09-18  NO 15/9-F-5 AH                5769           15/9-F-5   \n",
              "\n",
              "       NPD_FIELD_CODE NPD_FIELD_NAME  NPD_FACILITY_CODE NPD_FACILITY_NAME  \\\n",
              "0             3420717          VOLVE             369304    MÆRSK INSPIRER   \n",
              "1             3420717          VOLVE             369304    MÆRSK INSPIRER   \n",
              "2             3420717          VOLVE             369304    MÆRSK INSPIRER   \n",
              "3             3420717          VOLVE             369304    MÆRSK INSPIRER   \n",
              "4             3420717          VOLVE             369304    MÆRSK INSPIRER   \n",
              "...               ...            ...                ...               ...   \n",
              "15629         3420717          VOLVE             369304    MÆRSK INSPIRER   \n",
              "15630         3420717          VOLVE             369304    MÆRSK INSPIRER   \n",
              "15631         3420717          VOLVE             369304    MÆRSK INSPIRER   \n",
              "15632         3420717          VOLVE             369304    MÆRSK INSPIRER   \n",
              "15633         3420717          VOLVE             369304    MÆRSK INSPIRER   \n",
              "\n",
              "       ON_STREAM_HRS  AVG_DOWNHOLE_PRESSURE  ...  AVG_CHOKE_UOM  AVG_WHP_P  \\\n",
              "0                0.0                0.00000  ...              %    0.00000   \n",
              "1                0.0                    NaN  ...              %    0.00000   \n",
              "2                0.0                    NaN  ...              %    0.00000   \n",
              "3                0.0                    NaN  ...              %    0.00000   \n",
              "4                0.0              310.37614  ...              %   33.09788   \n",
              "...              ...                    ...  ...            ...        ...   \n",
              "15629            0.0                    NaN  ...              %    0.07776   \n",
              "15630            0.0                    NaN  ...              %    0.08545   \n",
              "15631            0.0                    NaN  ...              %    0.08544   \n",
              "15632            0.0                    NaN  ...              %    0.07497   \n",
              "15633            0.0                    NaN  ...            NaN        NaN   \n",
              "\n",
              "       AVG_WHT_P  DP_CHOKE_SIZE BORE_OIL_VOL  BORE_GAS_VOL  BORE_WAT_VOL  \\\n",
              "0        0.00000        0.00000          0.0           0.0           0.0   \n",
              "1        0.00000        0.00000          0.0           0.0           0.0   \n",
              "2        0.00000        0.00000          0.0           0.0           0.0   \n",
              "3        0.00000        0.00000          0.0           0.0           0.0   \n",
              "4       10.47992       33.07195          0.0           0.0           0.0   \n",
              "...          ...            ...          ...           ...           ...   \n",
              "15629    0.22879        0.01862          0.0           0.0           0.0   \n",
              "15630    0.22914        0.00631          0.0           0.0           0.0   \n",
              "15631    0.22896        0.01181          0.0           0.0           0.0   \n",
              "15632    0.22846        0.02576          0.0           0.0           0.0   \n",
              "15633        NaN        0.00000          NaN           NaN           NaN   \n",
              "\n",
              "       BORE_WI_VOL   FLOW_KIND  WELL_TYPE  \n",
              "0              NaN  production         WI  \n",
              "1              NaN  production         OP  \n",
              "2              NaN  production         OP  \n",
              "3              NaN  production         OP  \n",
              "4              NaN  production         OP  \n",
              "...            ...         ...        ...  \n",
              "15629          NaN  production         OP  \n",
              "15630          NaN  production         OP  \n",
              "15631          NaN  production         OP  \n",
              "15632          NaN  production         OP  \n",
              "15633          0.0   injection         WI  \n",
              "\n",
              "[15634 rows x 24 columns]"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "full_data = pd.read_excel('Volve_production_data.xlsx')\n",
        "full_data"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "111663d2",
      "metadata": {
        "id": "111663d2"
      },
      "source": [
        "Let's print out the names of all columns."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d9e7bb65",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d9e7bb65",
        "outputId": "a97b54ad-2899-4370-de03-530a457749f1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Index(['DATEPRD', 'WELL_BORE_CODE', 'NPD_WELL_BORE_CODE', 'NPD_WELL_BORE_NAME',\n",
            "       'NPD_FIELD_CODE', 'NPD_FIELD_NAME', 'NPD_FACILITY_CODE',\n",
            "       'NPD_FACILITY_NAME', 'ON_STREAM_HRS', 'AVG_DOWNHOLE_PRESSURE',\n",
            "       'AVG_DOWNHOLE_TEMPERATURE', 'AVG_DP_TUBING', 'AVG_ANNULUS_PRESS',\n",
            "       'AVG_CHOKE_SIZE_P', 'AVG_CHOKE_UOM', 'AVG_WHP_P', 'AVG_WHT_P',\n",
            "       'DP_CHOKE_SIZE', 'BORE_OIL_VOL', 'BORE_GAS_VOL', 'BORE_WAT_VOL',\n",
            "       'BORE_WI_VOL', 'FLOW_KIND', 'WELL_TYPE'],\n",
            "      dtype='object')\n"
          ]
        }
      ],
      "source": [
        "column_names = full_data.columns\n",
        "print(column_names)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2cd0ebbb",
      "metadata": {
        "id": "2cd0ebbb"
      },
      "source": [
        "The `Vovle` dataset has around 40,000 files, including:\n",
        "* Geophysical data, including interpretations\n",
        "* Geo-Science Archive\n",
        "* Production data\n",
        "* Report\n",
        "* Static models and dynamic simulations\n",
        "* Seismic data\n",
        "* Well logs\n",
        "* Well Technical Data\n",
        "* Realtime drilling data\n",
        "\n",
        "To optimize model performance, it is essential to extract all the relevant features from the dataset. This involves both spatial (static) and temporal (dynamic) data. For example, extracting features from the Surface Operational Conditions, Details of all Wells Architecture, Well Logs, Core Analysis, Well Tests, Seismic, Production Logs, Temperature Survey, Production/Injection History, etc. to build a comprehensive reservoir model.\n",
        "\n",
        "However, feature extraction is itself a comprehensive topic, and for the purpose of this discussion, my focus is on the machine learning (ML) aspect rather than delving deep into feature extraction. Therefore, we will use only dynamic (production) data such as: `ON_STREAM_HRS, AVG_DOWNHOLE_PRESSURE, AVG_DOWNHOLE_TEMPERATURE, AVG_DP_TUBING, AVG_ANNULUS_PRESS, AVG_CHOKE_SIZE_P, AVG_CHOKE_UOM, AVG_WHP_P, AVG_WHT_P, DP_CHOKE_SIZE,` and `BORE_OIL_VOL` to train our LSTM model.\n",
        "\n",
        "\n",
        "Alright, I will do the code for oil prediction, and later, you can easily adapt it for gas and water prediction with a simple modification.\n",
        "\n",
        "Let's see the names of different wells and their types:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d046c1ae",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d046c1ae",
        "outputId": "d06cd3d6-050f-4db4-ecae-c9dfab35d46e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "All Unique Well Bore Names:\n",
            "['15/9-F-1 C' '15/9-F-11' '15/9-F-12' '15/9-F-14' '15/9-F-15 D' '15/9-F-4'\n",
            " '15/9-F-5']\n",
            "\n",
            "Well Bore Names for 'production':\n",
            "['15/9-F-1 C' '15/9-F-11' '15/9-F-12' '15/9-F-14' '15/9-F-15 D' '15/9-F-5']\n",
            "\n",
            "Well Bore Names for 'injection':\n",
            "['15/9-F-4' '15/9-F-5']\n"
          ]
        }
      ],
      "source": [
        "unique_well_bore = full_data['NPD_WELL_BORE_NAME'].unique()\n",
        "print(\"All Unique Well Bore Names:\")\n",
        "print(unique_well_bore)\n",
        "\n",
        "unique_producer_well_bore = full_data[full_data['FLOW_KIND'] == 'production']['NPD_WELL_BORE_NAME'].unique()\n",
        "print(\"\\nWell Bore Names for 'production':\")\n",
        "print(unique_producer_well_bore)\n",
        "\n",
        "unique_injection_well_bore = full_data[full_data['FLOW_KIND'] == 'injection']['NPD_WELL_BORE_NAME'].unique()\n",
        "print(\"\\nWell Bore Names for 'injection':\")\n",
        "print(unique_injection_well_bore)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "6efe92cc",
      "metadata": {
        "id": "6efe92cc"
      },
      "source": [
        "Notice that Well 15/9-F-5 is on both list (production and injection). Because it was a water injector and then was converted into an oil producer on April 11, 2016. Moreover, the longest-producing well is 15/9-F-12. See [this dissertation](https://researchrepository.wvu.edu/etd/11970/) for more details.\n",
        "\n",
        "The below cell will sort the data in chronological order:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "26f96619",
      "metadata": {
        "id": "26f96619"
      },
      "outputs": [],
      "source": [
        "full_data = full_data.sort_values(by='DATEPRD')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c9d9456b",
      "metadata": {
        "id": "c9d9456b"
      },
      "source": [
        "Now let's define our data to use. I will use the data of the longest producing well (15/9-F-12), but if you want to use the full data, you just need to uncomment the `Full data` code and comment out the `F-12 data` code. And, if you want to train a model for gas or water, you just need to replace the `BORE_OIL_VOL` with `BORE_GAS_VOL` or `BORE_WAT_VOL` respectively.  \n",
        "The `DATEPRD` column is only for plotting purposes."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "21a33314",
      "metadata": {
        "id": "21a33314"
      },
      "outputs": [],
      "source": [
        "# F-12 data\n",
        "data = full_data[(full_data['NPD_WELL_BORE_NAME'] == '15/9-F-12')][['DATEPRD', 'ON_STREAM_HRS', 'AVG_DOWNHOLE_PRESSURE', 'AVG_DOWNHOLE_TEMPERATURE', 'AVG_DP_TUBING', 'AVG_ANNULUS_PRESS', 'AVG_CHOKE_SIZE_P', 'AVG_WHP_P', 'AVG_WHT_P', 'DP_CHOKE_SIZE', 'BORE_OIL_VOL']]\n",
        "\n",
        "# Full data\n",
        "#data = full_data[['DATEPRD', 'ON_STREAM_HRS', 'AVG_DOWNHOLE_PRESSURE', 'AVG_DOWNHOLE_TEMPERATURE', 'AVG_DP_TUBING', 'AVG_ANNULUS_PRESS', 'AVG_CHOKE_SIZE_P', 'AVG_WHP_P', 'AVG_WHT_P', 'DP_CHOKE_SIZE', 'BORE_OIL_VOL']]"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "6c2b0486",
      "metadata": {
        "id": "6c2b0486"
      },
      "source": [
        "Let's see how many samples we have:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9edac422",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9edac422",
        "outputId": "be7e05f0-6c2a-440e-aae6-921ac327ad45"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "We have 3056 samples\n"
          ]
        }
      ],
      "source": [
        "print(f'We have {data.shape[0]} samples')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d637e272",
      "metadata": {
        "id": "d637e272"
      },
      "source": [
        "Check if this **3056** has any null values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4a022924",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4a022924",
        "outputId": "e2b0aa6a-7f4e-4025-f8b8-b80ec3be6c36"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Null values:\n",
            "DATEPRD                      0\n",
            "ON_STREAM_HRS                0\n",
            "AVG_DOWNHOLE_PRESSURE        6\n",
            "AVG_DOWNHOLE_TEMPERATURE     6\n",
            "AVG_DP_TUBING                6\n",
            "AVG_ANNULUS_PRESS           13\n",
            "AVG_CHOKE_SIZE_P            44\n",
            "AVG_WHP_P                    0\n",
            "AVG_WHT_P                    0\n",
            "DP_CHOKE_SIZE                0\n",
            "BORE_OIL_VOL                 0\n",
            "dtype: int64\n"
          ]
        }
      ],
      "source": [
        "null_values = data.isnull().sum()\n",
        "print(\"Null values:\")\n",
        "print(null_values)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "10cd9e91",
      "metadata": {
        "id": "10cd9e91"
      },
      "source": [
        "Alright, we have 6 null values for `AVG_DOWNHOLE_PRESSURE`, `AVG_DOWNHOLE_TEMPERATURE`, `AVG_DP_TUBING` each and 13 & 44 null values in `AVG_ANNULUS_PRESS` and `AVG_CHOKE_SIZE_P` respectively.\n",
        "\n",
        "You may want to use different methods to patch the missing values but, I am simply deleting these cells as they are only 2% of the total data.  \n",
        "The below code will delete all the null values."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d44df838",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d44df838",
        "outputId": "62f42783-7d40-428f-d231-ff1dbbc5bad5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Null values:\n",
            "DATEPRD                     0\n",
            "ON_STREAM_HRS               0\n",
            "AVG_DOWNHOLE_PRESSURE       0\n",
            "AVG_DOWNHOLE_TEMPERATURE    0\n",
            "AVG_DP_TUBING               0\n",
            "AVG_ANNULUS_PRESS           0\n",
            "AVG_CHOKE_SIZE_P            0\n",
            "AVG_WHP_P                   0\n",
            "AVG_WHT_P                   0\n",
            "DP_CHOKE_SIZE               0\n",
            "BORE_OIL_VOL                0\n",
            "dtype: int64\n"
          ]
        }
      ],
      "source": [
        "data = data.dropna()\n",
        "null_values = data.isnull().sum()\n",
        "print(\"Null values:\")\n",
        "print(null_values)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e21796c5",
      "metadata": {
        "id": "e21796c5"
      },
      "source": [
        "Let's see our data:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "dd08833c",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "id": "dd08833c",
        "outputId": "fb9a7694-5d90-48ca-b044-b5cc90ea993e"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-5c543ec5-3b55-40e3-9d43-a12e923ca118\" class=\"colab-df-container\">\n",
              "    <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>DATEPRD</th>\n",
              "      <th>ON_STREAM_HRS</th>\n",
              "      <th>AVG_DOWNHOLE_PRESSURE</th>\n",
              "      <th>AVG_DOWNHOLE_TEMPERATURE</th>\n",
              "      <th>AVG_DP_TUBING</th>\n",
              "      <th>AVG_ANNULUS_PRESS</th>\n",
              "      <th>AVG_CHOKE_SIZE_P</th>\n",
              "      <th>AVG_WHP_P</th>\n",
              "      <th>AVG_WHT_P</th>\n",
              "      <th>DP_CHOKE_SIZE</th>\n",
              "      <th>BORE_OIL_VOL</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1911</th>\n",
              "      <td>2008-02-12</td>\n",
              "      <td>11.50</td>\n",
              "      <td>308.055940</td>\n",
              "      <td>104.418090</td>\n",
              "      <td>201.250000</td>\n",
              "      <td>18.639557</td>\n",
              "      <td>14.484431</td>\n",
              "      <td>106.805939</td>\n",
              "      <td>16.294718</td>\n",
              "      <td>92.475107</td>\n",
              "      <td>284.65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1912</th>\n",
              "      <td>2008-02-13</td>\n",
              "      <td>24.00</td>\n",
              "      <td>303.033518</td>\n",
              "      <td>105.402696</td>\n",
              "      <td>182.144942</td>\n",
              "      <td>16.466641</td>\n",
              "      <td>22.832986</td>\n",
              "      <td>120.888576</td>\n",
              "      <td>53.374330</td>\n",
              "      <td>89.620148</td>\n",
              "      <td>1869.70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1913</th>\n",
              "      <td>2008-02-14</td>\n",
              "      <td>22.50</td>\n",
              "      <td>295.586061</td>\n",
              "      <td>105.775486</td>\n",
              "      <td>181.867887</td>\n",
              "      <td>12.659589</td>\n",
              "      <td>31.249966</td>\n",
              "      <td>113.718175</td>\n",
              "      <td>72.737631</td>\n",
              "      <td>80.120465</td>\n",
              "      <td>3124.09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1914</th>\n",
              "      <td>2008-02-15</td>\n",
              "      <td>23.15</td>\n",
              "      <td>297.662702</td>\n",
              "      <td>105.751875</td>\n",
              "      <td>180.832060</td>\n",
              "      <td>5.264052</td>\n",
              "      <td>28.518930</td>\n",
              "      <td>116.830642</td>\n",
              "      <td>69.995943</td>\n",
              "      <td>84.250990</td>\n",
              "      <td>2608.28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1915</th>\n",
              "      <td>2008-02-16</td>\n",
              "      <td>24.00</td>\n",
              "      <td>295.935519</td>\n",
              "      <td>105.811114</td>\n",
              "      <td>180.501045</td>\n",
              "      <td>12.384634</td>\n",
              "      <td>30.227902</td>\n",
              "      <td>115.434474</td>\n",
              "      <td>71.518347</td>\n",
              "      <td>82.359026</td>\n",
              "      <td>3051.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4962</th>\n",
              "      <td>2016-09-13</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>19.958840</td>\n",
              "      <td>0.992907</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.012620</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4963</th>\n",
              "      <td>2016-09-14</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>19.958630</td>\n",
              "      <td>0.973044</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.015740</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4964</th>\n",
              "      <td>2016-09-15</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>19.958870</td>\n",
              "      <td>0.977784</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.023690</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4965</th>\n",
              "      <td>2016-09-16</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>19.958730</td>\n",
              "      <td>0.961007</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.024660</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4966</th>\n",
              "      <td>2016-09-17</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>19.958520</td>\n",
              "      <td>0.903394</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.025740</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2993 rows × 11 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5c543ec5-3b55-40e3-9d43-a12e923ca118')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-5c543ec5-3b55-40e3-9d43-a12e923ca118 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-5c543ec5-3b55-40e3-9d43-a12e923ca118');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-bb6030a9-a7c5-4d99-b5d4-46ccfb9c4ad8\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-bb6030a9-a7c5-4d99-b5d4-46ccfb9c4ad8')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-bb6030a9-a7c5-4d99-b5d4-46ccfb9c4ad8 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "        DATEPRD  ON_STREAM_HRS  AVG_DOWNHOLE_PRESSURE  \\\n",
              "1911 2008-02-12          11.50             308.055940   \n",
              "1912 2008-02-13          24.00             303.033518   \n",
              "1913 2008-02-14          22.50             295.586061   \n",
              "1914 2008-02-15          23.15             297.662702   \n",
              "1915 2008-02-16          24.00             295.935519   \n",
              "...         ...            ...                    ...   \n",
              "4962 2016-09-13           0.00               0.000000   \n",
              "4963 2016-09-14           0.00               0.000000   \n",
              "4964 2016-09-15           0.00               0.000000   \n",
              "4965 2016-09-16           0.00               0.000000   \n",
              "4966 2016-09-17           0.00               0.000000   \n",
              "\n",
              "      AVG_DOWNHOLE_TEMPERATURE  AVG_DP_TUBING  AVG_ANNULUS_PRESS  \\\n",
              "1911                104.418090     201.250000          18.639557   \n",
              "1912                105.402696     182.144942          16.466641   \n",
              "1913                105.775486     181.867887          12.659589   \n",
              "1914                105.751875     180.832060           5.264052   \n",
              "1915                105.811114     180.501045          12.384634   \n",
              "...                        ...            ...                ...   \n",
              "4962                  0.000000       0.000000          19.958840   \n",
              "4963                  0.000000       0.000000          19.958630   \n",
              "4964                  0.000000       0.000000          19.958870   \n",
              "4965                  0.000000       0.000000          19.958730   \n",
              "4966                  0.000000       0.000000          19.958520   \n",
              "\n",
              "      AVG_CHOKE_SIZE_P   AVG_WHP_P  AVG_WHT_P  DP_CHOKE_SIZE  BORE_OIL_VOL  \n",
              "1911         14.484431  106.805939  16.294718      92.475107        284.65  \n",
              "1912         22.832986  120.888576  53.374330      89.620148       1869.70  \n",
              "1913         31.249966  113.718175  72.737631      80.120465       3124.09  \n",
              "1914         28.518930  116.830642  69.995943      84.250990       2608.28  \n",
              "1915         30.227902  115.434474  71.518347      82.359026       3051.80  \n",
              "...                ...         ...        ...            ...           ...  \n",
              "4962          0.992907    0.000000   0.000000       0.012620          0.00  \n",
              "4963          0.973044    0.000000   0.000000       0.015740          0.00  \n",
              "4964          0.977784    0.000000   0.000000       0.023690          0.00  \n",
              "4965          0.961007    0.000000   0.000000       0.024660          0.00  \n",
              "4966          0.903394    0.000000   0.000000       0.025740          0.00  \n",
              "\n",
              "[2993 rows x 11 columns]"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "21fb97ab",
      "metadata": {
        "id": "21fb97ab"
      },
      "source": [
        "So, we have now zero null values, **2993** samples and 11 columns. Remember that the date column is only for plotting purpose, so, we have **10** features.\n",
        "\n",
        "Plot the oil production (`BORE_OIL_VOL`) against the date (`DATEPRD`)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "bfcf19bf",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 474
        },
        "id": "bfcf19bf",
        "outputId": "cf3aa611-84e6-4314-e514-621185d783f6"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 1500x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.set(style=\"whitegrid\")\n",
        "plt.figure(figsize=(15, 6))\n",
        "sns.lineplot(data=data, x='DATEPRD', y='BORE_OIL_VOL',errorbar=None)\n",
        "plt.xlabel('Date')\n",
        "plt.ylabel('Oil Volume')\n",
        "plt.title('Production Data - Date vs. Oil Volume')\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8c593166",
      "metadata": {
        "id": "8c593166"
      },
      "source": [
        "We are using using `MinMaxScaler` of sklearn to scale our features.  \n",
        "Make sure to replace the `BORE_OIL_VOL` with correct term if you are modeling for gas or water."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "978fd740",
      "metadata": {
        "id": "978fd740"
      },
      "outputs": [],
      "source": [
        "tf.keras.utils.set_random_seed(42)\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "columns_to_scale = ['ON_STREAM_HRS', 'AVG_DOWNHOLE_PRESSURE', 'AVG_DOWNHOLE_TEMPERATURE', 'AVG_DP_TUBING', 'AVG_ANNULUS_PRESS', 'AVG_CHOKE_SIZE_P', 'AVG_WHP_P', 'AVG_WHT_P', 'DP_CHOKE_SIZE', 'BORE_OIL_VOL']\n",
        "data_to_scale = data[columns_to_scale].copy()\n",
        "scaler = MinMaxScaler()\n",
        "data_scaled = scaler.fit_transform(data_to_scale)\n",
        "data_scaled_df = pd.DataFrame(data_scaled, columns=columns_to_scale)\n",
        "\n",
        "# Add the 'DATEPRD' column as the first column. Later, we will use it for plotting purpose.\n",
        "data_scaled_df.insert(0, 'DATEPRD', data['DATEPRD'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b332166e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        },
        "id": "b332166e",
        "outputId": "473a53c4-ab27-45a6-a210-1165d298e32a"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-ef2aca99-848d-4170-bb67-fef09cabd150\" class=\"colab-df-container\">\n",
              "    <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>DATEPRD</th>\n",
              "      <th>ON_STREAM_HRS</th>\n",
              "      <th>AVG_DOWNHOLE_PRESSURE</th>\n",
              "      <th>AVG_DOWNHOLE_TEMPERATURE</th>\n",
              "      <th>AVG_DP_TUBING</th>\n",
              "      <th>AVG_ANNULUS_PRESS</th>\n",
              "      <th>AVG_CHOKE_SIZE_P</th>\n",
              "      <th>AVG_WHP_P</th>\n",
              "      <th>AVG_WHT_P</th>\n",
              "      <th>DP_CHOKE_SIZE</th>\n",
              "      <th>BORE_OIL_VOL</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>NaT</td>\n",
              "      <td>0.460</td>\n",
              "      <td>0.978644</td>\n",
              "      <td>0.971263</td>\n",
              "      <td>0.719611</td>\n",
              "      <td>0.620908</td>\n",
              "      <td>0.144844</td>\n",
              "      <td>0.777839</td>\n",
              "      <td>0.176238</td>\n",
              "      <td>0.745028</td>\n",
              "      <td>0.048338</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NaT</td>\n",
              "      <td>0.960</td>\n",
              "      <td>0.962689</td>\n",
              "      <td>0.980421</td>\n",
              "      <td>0.651296</td>\n",
              "      <td>0.548525</td>\n",
              "      <td>0.228330</td>\n",
              "      <td>0.880400</td>\n",
              "      <td>0.577278</td>\n",
              "      <td>0.722027</td>\n",
              "      <td>0.317507</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NaT</td>\n",
              "      <td>0.900</td>\n",
              "      <td>0.939029</td>\n",
              "      <td>0.983889</td>\n",
              "      <td>0.650306</td>\n",
              "      <td>0.421708</td>\n",
              "      <td>0.312500</td>\n",
              "      <td>0.828179</td>\n",
              "      <td>0.786705</td>\n",
              "      <td>0.645493</td>\n",
              "      <td>0.530524</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NaT</td>\n",
              "      <td>0.926</td>\n",
              "      <td>0.945627</td>\n",
              "      <td>0.983669</td>\n",
              "      <td>0.646602</td>\n",
              "      <td>0.175352</td>\n",
              "      <td>0.285189</td>\n",
              "      <td>0.850847</td>\n",
              "      <td>0.757051</td>\n",
              "      <td>0.678770</td>\n",
              "      <td>0.442930</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NaT</td>\n",
              "      <td>0.960</td>\n",
              "      <td>0.940140</td>\n",
              "      <td>0.984220</td>\n",
              "      <td>0.645418</td>\n",
              "      <td>0.412548</td>\n",
              "      <td>0.302279</td>\n",
              "      <td>0.840679</td>\n",
              "      <td>0.773517</td>\n",
              "      <td>0.663528</td>\n",
              "      <td>0.518248</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2988</th>\n",
              "      <td>2011-02-08</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.664855</td>\n",
              "      <td>0.009929</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000102</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2989</th>\n",
              "      <td>2011-02-09</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.664848</td>\n",
              "      <td>0.009730</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000127</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2990</th>\n",
              "      <td>2011-02-10</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.664856</td>\n",
              "      <td>0.009778</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000191</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2991</th>\n",
              "      <td>2011-02-11</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.664852</td>\n",
              "      <td>0.009610</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000199</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2992</th>\n",
              "      <td>2011-02-12</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.664845</td>\n",
              "      <td>0.009034</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000207</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2993 rows × 11 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ef2aca99-848d-4170-bb67-fef09cabd150')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-ef2aca99-848d-4170-bb67-fef09cabd150 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-ef2aca99-848d-4170-bb67-fef09cabd150');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-368ee84d-8fff-436f-bf9a-80b5759b1dfb\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-368ee84d-8fff-436f-bf9a-80b5759b1dfb')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-368ee84d-8fff-436f-bf9a-80b5759b1dfb button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "        DATEPRD  ON_STREAM_HRS  AVG_DOWNHOLE_PRESSURE  \\\n",
              "0           NaT          0.460               0.978644   \n",
              "1           NaT          0.960               0.962689   \n",
              "2           NaT          0.900               0.939029   \n",
              "3           NaT          0.926               0.945627   \n",
              "4           NaT          0.960               0.940140   \n",
              "...         ...            ...                    ...   \n",
              "2988 2011-02-08          0.000               0.000000   \n",
              "2989 2011-02-09          0.000               0.000000   \n",
              "2990 2011-02-10          0.000               0.000000   \n",
              "2991 2011-02-11          0.000               0.000000   \n",
              "2992 2011-02-12          0.000               0.000000   \n",
              "\n",
              "      AVG_DOWNHOLE_TEMPERATURE  AVG_DP_TUBING  AVG_ANNULUS_PRESS  \\\n",
              "0                     0.971263       0.719611           0.620908   \n",
              "1                     0.980421       0.651296           0.548525   \n",
              "2                     0.983889       0.650306           0.421708   \n",
              "3                     0.983669       0.646602           0.175352   \n",
              "4                     0.984220       0.645418           0.412548   \n",
              "...                        ...            ...                ...   \n",
              "2988                  0.000000       0.000000           0.664855   \n",
              "2989                  0.000000       0.000000           0.664848   \n",
              "2990                  0.000000       0.000000           0.664856   \n",
              "2991                  0.000000       0.000000           0.664852   \n",
              "2992                  0.000000       0.000000           0.664845   \n",
              "\n",
              "      AVG_CHOKE_SIZE_P  AVG_WHP_P  AVG_WHT_P  DP_CHOKE_SIZE  BORE_OIL_VOL  \n",
              "0             0.144844   0.777839   0.176238       0.745028      0.048338  \n",
              "1             0.228330   0.880400   0.577278       0.722027      0.317507  \n",
              "2             0.312500   0.828179   0.786705       0.645493      0.530524  \n",
              "3             0.285189   0.850847   0.757051       0.678770      0.442930  \n",
              "4             0.302279   0.840679   0.773517       0.663528      0.518248  \n",
              "...                ...        ...        ...            ...           ...  \n",
              "2988          0.009929   0.000000   0.000000       0.000102      0.000000  \n",
              "2989          0.009730   0.000000   0.000000       0.000127      0.000000  \n",
              "2990          0.009778   0.000000   0.000000       0.000191      0.000000  \n",
              "2991          0.009610   0.000000   0.000000       0.000199      0.000000  \n",
              "2992          0.009034   0.000000   0.000000       0.000207      0.000000  \n",
              "\n",
              "[2993 rows x 11 columns]"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data_scaled_df"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "6f8fe78e",
      "metadata": {
        "id": "6f8fe78e"
      },
      "source": [
        "Our data has all values between 0 and 1."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "63aa9c09",
      "metadata": {
        "id": "63aa9c09"
      },
      "source": [
        "Here comes the important part:  \n",
        "In LSTM, we feed the data of the previous time step, and predict the target for the current time step. In our case, we will feed all the features, including oil production, of the previous time step (previous day) and we will predict the oil production for the current time step (for today).\n",
        "\n",
        "So, let's divide our data into features and target (label). Later, we will use `windowed_dataset` function to have features for previous time step and target for current time step.  \n",
        "\n",
        "Make sure to replace the `BORE_OIL_VOL` with correct term if you are modeling for gas or water."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7b8f960b",
      "metadata": {
        "id": "7b8f960b"
      },
      "outputs": [],
      "source": [
        "features = data_scaled_df[['ON_STREAM_HRS', 'AVG_DOWNHOLE_PRESSURE', 'AVG_DOWNHOLE_TEMPERATURE', 'AVG_DP_TUBING', 'AVG_ANNULUS_PRESS', 'AVG_CHOKE_SIZE_P', 'AVG_WHP_P', 'AVG_WHT_P', 'DP_CHOKE_SIZE', 'BORE_OIL_VOL']]\n",
        "target = data_scaled_df[['BORE_OIL_VOL']]"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5c9cf0af",
      "metadata": {
        "id": "5c9cf0af"
      },
      "source": [
        "LSTM expects Numpy data type, so the below code will convert our data, DataFrame type, into Numpy data type."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4805dbc0",
      "metadata": {
        "id": "4805dbc0"
      },
      "outputs": [],
      "source": [
        "features = features.to_numpy()\n",
        "target = target.to_numpy()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4d2ae506",
      "metadata": {
        "id": "4d2ae506"
      },
      "source": [
        "Now we will use sklearn to split our data into 70% training, 15% validation, and 15% test data set."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "02b6a7b8",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "02b6a7b8",
        "outputId": "234cc7fc-4a0f-451e-e5db-40af821d9cf9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Number of samples in train set: 2095\n",
            "Number of samples in validation set: 449\n",
            "Number of samples in test set: 449\n"
          ]
        }
      ],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_temp, Y_train, Y_temp = train_test_split(features, target, test_size=0.3, shuffle=False)\n",
        "X_val, X_test, Y_val, Y_test = train_test_split(X_temp, Y_temp, test_size=0.5, shuffle=False)\n",
        "X_train = np.array(X_train)\n",
        "Y_train = np.array(Y_train)\n",
        "X_val = np.array(X_val)\n",
        "Y_val = np.array(Y_val)\n",
        "X_test = np.array(X_test)\n",
        "Y_test = np.array(Y_test)\n",
        "\n",
        "print(\"Number of samples in train set:\", X_train.shape[0])\n",
        "print(\"Number of samples in validation set:\", X_val.shape[0])\n",
        "print(\"Number of samples in test set:\", X_test.shape[0])"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "04b5ee0f",
      "metadata": {
        "id": "04b5ee0f"
      },
      "source": [
        "Alright! So for so good. Let's visualize our data after the split."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "011b9324",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 474
        },
        "id": "011b9324",
        "outputId": "573434c6-eb2d-4a9e-d887-9e4a60aaa665"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 1500x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(15, 6))\n",
        "\n",
        "sns.lineplot(x=data['DATEPRD'][:Y_train.shape[0]].ravel(), y=Y_train.ravel(), label='Train', errorbar=None)\n",
        "sns.lineplot(x=data['DATEPRD'][Y_train.shape[0]:Y_train.shape[0] + Y_val.shape[0]].ravel(), y=Y_val.ravel(), label='Val', errorbar=None)\n",
        "sns.lineplot(x=data['DATEPRD'][Y_train.shape[0] + Y_val.shape[0]:Y_train.shape[0] + Y_val.shape[0] + Y_test.shape[0]].ravel(), y=Y_test.ravel(), label='Test', errorbar=None)\n",
        "\n",
        "plt.xlabel('Date')\n",
        "plt.ylabel('Oil Volume')\n",
        "plt.title('Production Data - Date vs. Oil Volume')\n",
        "plt.grid(True)\n",
        "plt.legend()\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "70bf4ea6",
      "metadata": {
        "id": "70bf4ea6"
      },
      "source": [
        "Okay! The below code will divide features and target into previous and current time steps. This is the major concept of the LSTM.\n",
        "Our data will be:\n",
        "* All the features (including oil production) of the previous day, target (oil production) of the current day\n",
        "* Then all the features of the current day, target (oil production) of the next day\n",
        "* and so on...\n",
        "\n",
        "If you are not familiar with the concept, I have an explanation of the code below. Please click on the green text \"Click here for Explanation\" and you will see the text. Click again and it will hide the text."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "deb04d29",
      "metadata": {
        "id": "deb04d29"
      },
      "outputs": [],
      "source": [
        "def windowed_dataset(features, target, window_size, batch_size):\n",
        "    dataset = tf.data.Dataset.from_tensor_slices((features, target))\n",
        "    dataset = dataset.window(window_size + 1, shift=1, drop_remainder=True)\n",
        "    dataset = dataset.flat_map(lambda x, y: tf.data.Dataset.zip((x.batch(window_size + 1), y.batch(window_size + 1))))\n",
        "    dataset = dataset.map(lambda x, y: (x[:-1], y[1:]))\n",
        "    dataset = dataset.batch(batch_size).prefetch(1)\n",
        "    return dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b4bb4ae2",
      "metadata": {
        "id": "b4bb4ae2"
      },
      "source": [
        "<details>\n",
        "  <summary><font size=\"3\" color=\"darkgreen\"><b>Click here for Explanation</b></font></summary>\n",
        "  \n",
        "  Here's a step-by-step explanation of each part of the code:\n",
        "\n",
        "`def windowed_dataset(features, target, window_size, batch_size):`\n",
        "    This line defines a Python function named windowed_dataset that takes four arguments: features, target, window_size, and batch_size.\n",
        "\n",
        "`dataset = tf.data.Dataset.from_tensor_slices((features, target))`\n",
        "    This line creates a TensorFlow dataset using tf.data.Dataset.from_tensor_slices. It takes two arguments, features and target, and creates a dataset where each element is a pair consisting of a feature and its corresponding target. This is a common way to create a dataset from arrays.\n",
        "\n",
        "`dataset = dataset.window(window_size + 1, shift=1, drop_remainder=True)`\n",
        "This line applies a windowing operation to the dataset. It divides the dataset into overlapping windows of size `window_size + 1,` where window_size is a parameter provided to the function. The `shift=1` argument means that the windows will shift by one element at a time, creating overlapping windows. `drop_remainder=True` ensures that windows with fewer than `window_size + 1` elements are dropped.\n",
        "\n",
        "`dataset = dataset.flat_map(lambda x, y: tf.data.Dataset.zip((x.batch(window_size + 1), y.batch(window_size + 1))))`\n",
        "This line flattens the nested dataset created by the window operation. It uses flat_map to convert each window into a new dataset using a lambda function. Within this lambda function, x and y represent a window of features and target, respectively. `tf.data.Dataset.zip` is used to create pairs of (features_window, target_window), where each window contains `window_size + 1` elements. This essentially stacks the elements of each window together.\n",
        "\n",
        "`dataset = dataset.map(lambda x, y: (x[:-1], y[1:]))`\n",
        "This line maps a lambda function over the dataset to extract the input-output pairs for training. It takes each pair (features_window, target_window) and returns a new pair (input_sequence, target_sequence) where input_sequence is all elements of features_window except the last one `(x[:-1])`, and target_sequence is all elements of target_window except the first one `(y[1:])`. This prepares the data for a sequence prediction task where the model predicts the next element in a sequence based on the previous elements.\n",
        "\n",
        "`dataset = dataset.batch(batch_size).prefetch(1)`\n",
        "This line batches the dataset into mini-batches of size batch_size. Batching is commonly used in machine learning to process data in smaller chunks. The `prefetch(1)` operation prefetches one batch asynchronously to improve data loading efficiency.\n",
        "return dataset\n",
        "\n",
        "Finally, the function returns the prepared dataset, which is ready to be used for training a machine learning model.\n",
        "</details>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9e2c50ae",
      "metadata": {
        "id": "9e2c50ae"
      },
      "source": [
        "Now apply the above function to our data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b707e329",
      "metadata": {
        "id": "b707e329",
        "scrolled": false
      },
      "outputs": [],
      "source": [
        "tf.keras.utils.set_random_seed(42)\n",
        "window_size = 5\n",
        "batch_size = 32\n",
        "train_set = windowed_dataset(X_train, Y_train, window_size, batch_size)\n",
        "val_set = windowed_dataset(X_val, Y_val, window_size, batch_size)\n",
        "test_set = windowed_dataset(X_test, Y_test, window_size, batch_size)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "bdb73e97",
      "metadata": {
        "id": "bdb73e97"
      },
      "source": [
        "Now check the shape of our features and target, after applying the window function."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d8a78346",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d8a78346",
        "outputId": "eb2eb6d0-3e51-44e4-de8f-a0a939ad704a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Features shape: (32, 5, 10)\n",
            "Target shape: (32, 5, 1)\n"
          ]
        }
      ],
      "source": [
        "train_iterator = iter(train_set)\n",
        "first_element = next(train_iterator)\n",
        "features_shape = first_element[0].shape\n",
        "target_shape = first_element[1].shape\n",
        "\n",
        "print(\"Features shape:\", features_shape)\n",
        "print(\"Target shape:\", target_shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "78144d08",
      "metadata": {
        "id": "78144d08"
      },
      "source": [
        "The shape of **input** is **`(batch_size, window_size, features)`**  \n",
        "The shape of **output** is **`(batch_size, window_size, target)`**\n",
        "\n",
        "- `batch_size` is 32, which means we process 32 samples simultaneously.\n",
        "- `window_size` is 5, indicating that each sample spans 5 time steps or days.\n",
        "- `features` is 10, representing the number of input data points at each time step.\n",
        "- `target` is 1, representing oil production for each time step.\n",
        "\n",
        "To provide a clearer understanding, let's examine the data for the first batch. Run the below cell to see:\n",
        "\n",
        "- We have 5 time steps (5 days), each consisting of 10 input data points (features) and 1 target value.\n",
        "- Note that out of 10 input data, the last one is the oil production of the previous day.\n",
        "- **You can observe that the target (oil production) for day 1 is become an input for day 2.**\n",
        "- **Target for day 2 is become an input for day 3 and this pattern continues for subsequent days.**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "cc450de2",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cc450de2",
        "outputId": "ff38c473-e3cb-4139-9680-19f2608e249c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[1;34mDay 1 Data:\u001b[0m\n",
            "Input data for day 1: [0.46       0.97864427 0.97126284 0.71961053 0.62090818 0.14484431\n",
            " 0.77783947 0.17623791 0.74502803 0.04833843]\n",
            "Target (oil production) for day 2: [0.31750695]\n",
            "\n",
            "\u001b[1;34mDay 2 Data:\u001b[0m\n",
            "Input data for day 2: [0.96       0.96268884 0.98042132 0.65129648 0.54852549 0.22832986\n",
            " 0.8803996  0.57727789 0.72202698 0.31750695]\n",
            "Target (oil production) for day 3: [0.53052377]\n",
            "\n",
            "\u001b[1;34mDay 3 Data:\u001b[0m\n",
            "Input data for day 3: [0.9        0.93902947 0.9838889  0.65030582 0.42170758 0.31249966\n",
            " 0.82817946 0.78670451 0.64549255 0.53052377]\n",
            "Target (oil production) for day 4: [0.44293043]\n",
            "\n",
            "\u001b[1;34mDay 4 Data:\u001b[0m\n",
            "Input data for day 4: [0.926      0.94562662 0.98366927 0.64660201 0.17535249 0.2851893\n",
            " 0.85084674 0.75705139 0.67877023 0.44293043]\n",
            "Target (oil production) for day 5: [0.51824769]\n",
            "\n",
            "\u001b[1;34mDay 5 Data:\u001b[0m\n",
            "Input data for day 5: [0.96       0.94013964 0.9842203  0.64541839 0.41254845 0.30227902\n",
            " 0.84067882 0.77351717 0.66352757 0.51824769]\n",
            "Target (oil production) for day 6: [0.50650145]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "first_sample = (first_element[0][0], first_element[1][0])\n",
        "\n",
        "for day in range(len(first_sample[0])):\n",
        "    print(f\"\\033[1;34mDay {day + 1} Data:\\033[0m\")\n",
        "    print(f\"Input data for day {day + 1}: {first_sample[0][day]}\")\n",
        "    print(f\"Target (oil production) for day {day + 2}: {first_sample[1][day]}\")\n",
        "    print()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3b1d17af",
      "metadata": {
        "id": "3b1d17af"
      },
      "source": [
        "Phew! We did a lot of work.\n",
        "\n",
        "Below, we will define our LSTM model. After trying out the different architectures, I found that the below gave the best result."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e45cec12",
      "metadata": {
        "id": "e45cec12"
      },
      "outputs": [],
      "source": [
        "def LSTM_model():\n",
        "    model = tf.keras.models.Sequential([\n",
        "        tf.keras.layers.LSTM(128,return_sequences=True),\n",
        "        tf.keras.layers.Dropout(0.5),\n",
        "        tf.keras.layers.LSTM(64),\n",
        "        tf.keras.layers.Dropout(0.5),\n",
        "        tf.keras.layers.Dense(1)\n",
        "    ])\n",
        "\n",
        "    return model"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4059a856",
      "metadata": {
        "id": "4059a856"
      },
      "source": [
        "Now start training our model. I am using `EarlyStopping` from TensorFlow to stop our training early when we do not see any significant improvement on validation data set.\n",
        "\n",
        "The below cell may take 10 minutes to an hour, depending on your GPU/CPU."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6cf89773",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6cf89773",
        "outputId": "99762f67-cf2f-4c34-95ae-f1fc29211394",
        "scrolled": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/1000\n",
            "66/66 [==============================] - 9s 39ms/step - loss: 0.1229 - val_loss: 0.0148\n",
            "Epoch 2/1000\n",
            "66/66 [==============================] - 1s 19ms/step - loss: 0.0560 - val_loss: 0.0087\n",
            "Epoch 3/1000\n",
            "66/66 [==============================] - 3s 46ms/step - loss: 0.0383 - val_loss: 0.0060\n",
            "Epoch 4/1000\n",
            "66/66 [==============================] - 2s 34ms/step - loss: 0.0317 - val_loss: 0.0050\n",
            "Epoch 5/1000\n",
            "66/66 [==============================] - 1s 22ms/step - loss: 0.0304 - val_loss: 0.0041\n",
            "Epoch 6/1000\n",
            "66/66 [==============================] - 1s 22ms/step - loss: 0.0255 - val_loss: 0.0037\n",
            "Epoch 7/1000\n",
            "66/66 [==============================] - 1s 22ms/step - loss: 0.0241 - val_loss: 0.0030\n",
            "Epoch 8/1000\n",
            "66/66 [==============================] - 1s 20ms/step - loss: 0.0225 - val_loss: 0.0024\n",
            "Epoch 9/1000\n",
            "66/66 [==============================] - 2s 27ms/step - loss: 0.0196 - val_loss: 0.0019\n",
            "Epoch 10/1000\n",
            "66/66 [==============================] - 3s 52ms/step - loss: 0.0180 - val_loss: 0.0016\n",
            "Epoch 11/1000\n",
            "66/66 [==============================] - 3s 38ms/step - loss: 0.0160 - val_loss: 0.0013\n",
            "Epoch 12/1000\n",
            "66/66 [==============================] - 1s 22ms/step - loss: 0.0153 - val_loss: 0.0010\n",
            "Epoch 13/1000\n",
            "66/66 [==============================] - 1s 21ms/step - loss: 0.0150 - val_loss: 8.1044e-04\n",
            "Epoch 14/1000\n",
            "66/66 [==============================] - 1s 21ms/step - loss: 0.0136 - val_loss: 6.5939e-04\n",
            "Epoch 15/1000\n",
            "66/66 [==============================] - 2s 25ms/step - loss: 0.0124 - val_loss: 5.3339e-04\n",
            "Epoch 16/1000\n",
            "66/66 [==============================] - 2s 34ms/step - loss: 0.0135 - val_loss: 5.1442e-04\n",
            "Epoch 17/1000\n",
            "66/66 [==============================] - 2s 34ms/step - loss: 0.0126 - val_loss: 4.4063e-04\n",
            "Epoch 18/1000\n",
            "66/66 [==============================] - 2s 30ms/step - loss: 0.0131 - val_loss: 6.3049e-04\n",
            "Epoch 19/1000\n",
            "66/66 [==============================] - 1s 19ms/step - loss: 0.0130 - val_loss: 6.6048e-04\n",
            "Epoch 20/1000\n",
            "66/66 [==============================] - 1s 19ms/step - loss: 0.0125 - val_loss: 7.0340e-04\n",
            "Epoch 21/1000\n",
            "66/66 [==============================] - 1s 20ms/step - loss: 0.0121 - val_loss: 9.7778e-04\n",
            "Epoch 22/1000\n",
            "66/66 [==============================] - 1s 21ms/step - loss: 0.0125 - val_loss: 0.0012\n",
            "Epoch 23/1000\n",
            "66/66 [==============================] - 1s 22ms/step - loss: 0.0123 - val_loss: 0.0012\n",
            "Epoch 24/1000\n",
            "66/66 [==============================] - 1s 21ms/step - loss: 0.0118 - val_loss: 0.0013\n",
            "Epoch 25/1000\n",
            "66/66 [==============================] - 2s 35ms/step - loss: 0.0119 - val_loss: 0.0019\n",
            "Epoch 26/1000\n",
            "66/66 [==============================] - 2s 37ms/step - loss: 0.0119 - val_loss: 0.0016\n",
            "Epoch 27/1000\n",
            "66/66 [==============================] - 1s 19ms/step - loss: 0.0108 - val_loss: 0.0015\n",
            "Epoch 28/1000\n",
            "66/66 [==============================] - 1s 19ms/step - loss: 0.0113 - val_loss: 0.0014\n",
            "Epoch 29/1000\n",
            "66/66 [==============================] - 1s 22ms/step - loss: 0.0116 - val_loss: 0.0015\n",
            "Epoch 30/1000\n",
            "66/66 [==============================] - 1s 21ms/step - loss: 0.0108 - val_loss: 0.0015\n",
            "Epoch 31/1000\n",
            "66/66 [==============================] - 2s 25ms/step - loss: 0.0107 - val_loss: 0.0012\n",
            "Epoch 32/1000\n",
            "66/66 [==============================] - 2s 35ms/step - loss: 0.0113 - val_loss: 0.0014\n",
            "Epoch 33/1000\n",
            "66/66 [==============================] - 3s 51ms/step - loss: 0.0108 - val_loss: 0.0014\n",
            "Epoch 34/1000\n",
            "66/66 [==============================] - 3s 40ms/step - loss: 0.0104 - val_loss: 0.0012\n",
            "Epoch 35/1000\n",
            "66/66 [==============================] - 1s 22ms/step - loss: 0.0109 - val_loss: 0.0012\n",
            "Epoch 36/1000\n",
            "66/66 [==============================] - 1s 19ms/step - loss: 0.0106 - val_loss: 0.0012\n",
            "Epoch 37/1000\n",
            "66/66 [==============================] - 1s 20ms/step - loss: 0.0108 - val_loss: 0.0014\n",
            "Epoch 38/1000\n",
            "66/66 [==============================] - 1s 21ms/step - loss: 0.0104 - val_loss: 0.0013\n",
            "Epoch 39/1000\n",
            "66/66 [==============================] - 2s 33ms/step - loss: 0.0106 - val_loss: 0.0011\n",
            "Epoch 40/1000\n",
            "66/66 [==============================] - 2s 36ms/step - loss: 0.0108 - val_loss: 0.0010\n",
            "Epoch 41/1000\n",
            "66/66 [==============================] - 2s 34ms/step - loss: 0.0103 - val_loss: 0.0010\n",
            "Epoch 42/1000\n",
            "66/66 [==============================] - 2s 22ms/step - loss: 0.0106 - val_loss: 0.0012\n",
            "Epoch 43/1000\n",
            "66/66 [==============================] - 1s 21ms/step - loss: 0.0108 - val_loss: 0.0011\n",
            "Epoch 44/1000\n",
            "66/66 [==============================] - 1s 22ms/step - loss: 0.0106 - val_loss: 0.0011\n",
            "Epoch 45/1000\n",
            "66/66 [==============================] - 1s 22ms/step - loss: 0.0099 - val_loss: 0.0013\n",
            "Epoch 46/1000\n",
            "66/66 [==============================] - 2s 26ms/step - loss: 0.0101 - val_loss: 0.0011\n",
            "Epoch 47/1000\n",
            "66/66 [==============================] - 2s 33ms/step - loss: 0.0098 - val_loss: 0.0013\n",
            "Epoch 48/1000\n",
            "66/66 [==============================] - 2s 32ms/step - loss: 0.0099 - val_loss: 0.0013\n",
            "Epoch 49/1000\n",
            "66/66 [==============================] - 2s 25ms/step - loss: 0.0102 - val_loss: 0.0016\n",
            "Epoch 50/1000\n",
            "66/66 [==============================] - 1s 18ms/step - loss: 0.0096 - val_loss: 0.0011\n",
            "Epoch 51/1000\n",
            "66/66 [==============================] - 1s 20ms/step - loss: 0.0101 - val_loss: 0.0010\n",
            "Epoch 52/1000\n",
            "66/66 [==============================] - 1s 20ms/step - loss: 0.0098 - val_loss: 9.6946e-04\n",
            "Epoch 53/1000\n",
            "66/66 [==============================] - 1s 21ms/step - loss: 0.0097 - val_loss: 0.0013\n",
            "Epoch 54/1000\n",
            "66/66 [==============================] - 1s 19ms/step - loss: 0.0099 - val_loss: 0.0010\n",
            "Epoch 55/1000\n",
            "66/66 [==============================] - 2s 35ms/step - loss: 0.0096 - val_loss: 0.0013\n",
            "Epoch 56/1000\n",
            "66/66 [==============================] - 2s 34ms/step - loss: 0.0096 - val_loss: 0.0013\n",
            "Epoch 57/1000\n",
            "66/66 [==============================] - 2s 36ms/step - loss: 0.0100 - val_loss: 9.0910e-04\n",
            "Epoch 58/1000\n",
            "66/66 [==============================] - 1s 22ms/step - loss: 0.0095 - val_loss: 9.9139e-04\n",
            "Epoch 59/1000\n",
            "66/66 [==============================] - 1s 21ms/step - loss: 0.0092 - val_loss: 0.0011\n",
            "Epoch 60/1000\n",
            "66/66 [==============================] - 1s 19ms/step - loss: 0.0095 - val_loss: 0.0011\n",
            "Epoch 61/1000\n",
            "66/66 [==============================] - 1s 21ms/step - loss: 0.0093 - val_loss: 9.8033e-04\n",
            "Epoch 62/1000\n",
            "66/66 [==============================] - 1s 20ms/step - loss: 0.0094 - val_loss: 7.1862e-04\n",
            "Epoch 63/1000\n",
            "66/66 [==============================] - 1s 21ms/step - loss: 0.0093 - val_loss: 8.1418e-04\n",
            "Epoch 64/1000\n",
            "66/66 [==============================] - 2s 33ms/step - loss: 0.0094 - val_loss: 8.0914e-04\n",
            "Epoch 65/1000\n",
            "66/66 [==============================] - 2s 36ms/step - loss: 0.0093 - val_loss: 7.4711e-04\n",
            "Epoch 66/1000\n",
            "66/66 [==============================] - 1s 21ms/step - loss: 0.0093 - val_loss: 7.4485e-04\n",
            "Epoch 67/1000\n",
            "66/66 [==============================] - 1s 19ms/step - loss: 0.0089 - val_loss: 7.5847e-04\n",
            "Epoch 68/1000\n",
            "66/66 [==============================] - 1s 22ms/step - loss: 0.0092 - val_loss: 7.2640e-04\n",
            "Epoch 69/1000\n",
            "66/66 [==============================] - 1s 22ms/step - loss: 0.0088 - val_loss: 6.7996e-04\n",
            "Epoch 70/1000\n",
            "66/66 [==============================] - 2s 27ms/step - loss: 0.0089 - val_loss: 6.5823e-04\n",
            "Epoch 71/1000\n",
            "66/66 [==============================] - 2s 35ms/step - loss: 0.0089 - val_loss: 4.8633e-04\n",
            "Epoch 72/1000\n",
            "66/66 [==============================] - 2s 36ms/step - loss: 0.0087 - val_loss: 4.4272e-04\n",
            "Epoch 73/1000\n",
            "66/66 [==============================] - 2s 32ms/step - loss: 0.0089 - val_loss: 4.6336e-04\n",
            "Epoch 74/1000\n",
            "66/66 [==============================] - 1s 22ms/step - loss: 0.0086 - val_loss: 4.5411e-04\n",
            "Epoch 75/1000\n",
            "66/66 [==============================] - 1s 19ms/step - loss: 0.0088 - val_loss: 4.3868e-04\n",
            "Epoch 76/1000\n",
            "66/66 [==============================] - 1s 20ms/step - loss: 0.0086 - val_loss: 4.7990e-04\n",
            "Epoch 77/1000\n",
            "66/66 [==============================] - 1s 22ms/step - loss: 0.0085 - val_loss: 4.2666e-04\n",
            "Epoch 78/1000\n",
            "66/66 [==============================] - 2s 34ms/step - loss: 0.0088 - val_loss: 3.6734e-04\n",
            "Epoch 79/1000\n",
            "66/66 [==============================] - 2s 36ms/step - loss: 0.0084 - val_loss: 4.3678e-04\n",
            "Epoch 80/1000\n",
            "66/66 [==============================] - 1s 22ms/step - loss: 0.0085 - val_loss: 4.3238e-04\n",
            "Epoch 81/1000\n",
            "66/66 [==============================] - 1s 20ms/step - loss: 0.0085 - val_loss: 3.6971e-04\n",
            "Epoch 82/1000\n",
            "66/66 [==============================] - 1s 22ms/step - loss: 0.0087 - val_loss: 4.5512e-04\n",
            "Epoch 83/1000\n",
            "66/66 [==============================] - 1s 20ms/step - loss: 0.0081 - val_loss: 4.4270e-04\n",
            "Epoch 84/1000\n",
            "66/66 [==============================] - 1s 22ms/step - loss: 0.0087 - val_loss: 5.2159e-04\n",
            "Epoch 85/1000\n",
            "66/66 [==============================] - 2s 34ms/step - loss: 0.0080 - val_loss: 4.4866e-04\n",
            "Epoch 86/1000\n",
            "66/66 [==============================] - 2s 32ms/step - loss: 0.0081 - val_loss: 4.0637e-04\n",
            "Epoch 87/1000\n",
            "66/66 [==============================] - 2s 36ms/step - loss: 0.0086 - val_loss: 3.5066e-04\n",
            "Epoch 88/1000\n",
            "66/66 [==============================] - 1s 21ms/step - loss: 0.0079 - val_loss: 3.2691e-04\n",
            "Epoch 89/1000\n",
            "66/66 [==============================] - 1s 20ms/step - loss: 0.0083 - val_loss: 2.8091e-04\n",
            "Epoch 90/1000\n",
            "66/66 [==============================] - 1s 21ms/step - loss: 0.0083 - val_loss: 2.5028e-04\n",
            "Epoch 91/1000\n",
            "66/66 [==============================] - 2s 23ms/step - loss: 0.0081 - val_loss: 2.5429e-04\n",
            "Epoch 92/1000\n",
            "66/66 [==============================] - 2s 35ms/step - loss: 0.0078 - val_loss: 2.4475e-04\n",
            "Epoch 93/1000\n",
            "66/66 [==============================] - 2s 36ms/step - loss: 0.0079 - val_loss: 2.3913e-04\n",
            "Epoch 94/1000\n",
            "66/66 [==============================] - 2s 28ms/step - loss: 0.0083 - val_loss: 2.5918e-04\n",
            "Epoch 95/1000\n",
            "66/66 [==============================] - 1s 20ms/step - loss: 0.0079 - val_loss: 2.5426e-04\n",
            "Epoch 96/1000\n",
            "66/66 [==============================] - 1s 19ms/step - loss: 0.0084 - val_loss: 2.6201e-04\n",
            "Epoch 97/1000\n",
            "66/66 [==============================] - 1s 22ms/step - loss: 0.0078 - val_loss: 2.4531e-04\n",
            "Epoch 98/1000\n",
            "66/66 [==============================] - 1s 20ms/step - loss: 0.0078 - val_loss: 2.6408e-04\n",
            "Epoch 99/1000\n",
            "66/66 [==============================] - 2s 29ms/step - loss: 0.0080 - val_loss: 2.8082e-04\n",
            "Epoch 100/1000\n",
            "66/66 [==============================] - 2s 33ms/step - loss: 0.0079 - val_loss: 2.5719e-04\n",
            "Epoch 101/1000\n",
            "66/66 [==============================] - 2s 34ms/step - loss: 0.0078 - val_loss: 2.5319e-04\n",
            "Epoch 102/1000\n",
            "66/66 [==============================] - 2s 23ms/step - loss: 0.0080 - val_loss: 2.5489e-04\n",
            "Epoch 103/1000\n",
            "66/66 [==============================] - 1s 20ms/step - loss: 0.0078 - val_loss: 2.5550e-04\n",
            "Epoch 104/1000\n",
            "66/66 [==============================] - 1s 19ms/step - loss: 0.0076 - val_loss: 2.5448e-04\n",
            "Epoch 105/1000\n",
            "66/66 [==============================] - 1s 22ms/step - loss: 0.0079 - val_loss: 2.5337e-04\n",
            "Epoch 106/1000\n",
            "66/66 [==============================] - 1s 19ms/step - loss: 0.0080 - val_loss: 2.4511e-04\n",
            "Epoch 107/1000\n",
            "66/66 [==============================] - 2s 35ms/step - loss: 0.0078 - val_loss: 2.7277e-04\n",
            "Epoch 108/1000\n",
            "66/66 [==============================] - 2s 34ms/step - loss: 0.0078 - val_loss: 2.5080e-04\n",
            "Epoch 109/1000\n",
            "66/66 [==============================] - 2s 37ms/step - loss: 0.0078 - val_loss: 2.5484e-04\n",
            "Epoch 110/1000\n",
            "66/66 [==============================] - 1s 19ms/step - loss: 0.0076 - val_loss: 2.4371e-04\n",
            "Epoch 111/1000\n",
            "66/66 [==============================] - 1s 21ms/step - loss: 0.0079 - val_loss: 2.5495e-04\n",
            "Epoch 112/1000\n",
            "66/66 [==============================] - 1s 22ms/step - loss: 0.0078 - val_loss: 2.5091e-04\n",
            "Epoch 113/1000\n",
            "66/66 [==============================] - 2s 29ms/step - loss: 0.0078 - val_loss: 2.4553e-04\n",
            "Epoch 114/1000\n",
            "66/66 [==============================] - 2s 31ms/step - loss: 0.0075 - val_loss: 2.4730e-04\n",
            "Epoch 115/1000\n",
            "66/66 [==============================] - 4s 53ms/step - loss: 0.0074 - val_loss: 2.4222e-04\n",
            "Epoch 116/1000\n",
            "66/66 [==============================] - 3s 38ms/step - loss: 0.0075 - val_loss: 2.4379e-04\n",
            "Epoch 117/1000\n",
            "66/66 [==============================] - 2s 25ms/step - loss: 0.0075 - val_loss: 2.5002e-04\n",
            "Epoch 118/1000\n",
            "66/66 [==============================] - 2s 29ms/step - loss: 0.0076 - val_loss: 3.3638e-04\n",
            "Epoch 119/1000\n",
            "66/66 [==============================] - 2s 26ms/step - loss: 0.0074 - val_loss: 2.6468e-04\n",
            "Epoch 120/1000\n",
            "66/66 [==============================] - 2s 31ms/step - loss: 0.0076 - val_loss: 2.5736e-04\n",
            "Epoch 121/1000\n",
            "66/66 [==============================] - 3s 42ms/step - loss: 0.0074 - val_loss: 2.5774e-04\n",
            "Epoch 122/1000\n",
            "66/66 [==============================] - 3s 39ms/step - loss: 0.0075 - val_loss: 2.5146e-04\n",
            "Epoch 123/1000\n",
            "66/66 [==============================] - 2s 25ms/step - loss: 0.0074 - val_loss: 2.4096e-04\n",
            "Epoch 124/1000\n",
            "66/66 [==============================] - 2s 25ms/step - loss: 0.0074 - val_loss: 2.3593e-04\n",
            "Epoch 125/1000\n",
            "66/66 [==============================] - 2s 24ms/step - loss: 0.0077 - val_loss: 2.4669e-04\n",
            "Epoch 126/1000\n",
            "66/66 [==============================] - 2s 26ms/step - loss: 0.0071 - val_loss: 2.4370e-04\n",
            "Epoch 127/1000\n",
            "66/66 [==============================] - 3s 44ms/step - loss: 0.0073 - val_loss: 2.5451e-04\n",
            "Epoch 128/1000\n",
            "66/66 [==============================] - 3s 49ms/step - loss: 0.0075 - val_loss: 2.9438e-04\n",
            "Epoch 129/1000\n",
            "66/66 [==============================] - 2s 26ms/step - loss: 0.0074 - val_loss: 2.4757e-04\n",
            "Epoch 130/1000\n",
            "66/66 [==============================] - 1s 22ms/step - loss: 0.0075 - val_loss: 2.3847e-04\n",
            "Epoch 131/1000\n",
            "66/66 [==============================] - 1s 22ms/step - loss: 0.0076 - val_loss: 2.4443e-04\n",
            "Epoch 132/1000\n",
            "66/66 [==============================] - 1s 20ms/step - loss: 0.0073 - val_loss: 2.5028e-04\n",
            "Epoch 133/1000\n",
            "66/66 [==============================] - 3s 42ms/step - loss: 0.0073 - val_loss: 2.5194e-04\n",
            "Epoch 134/1000\n",
            "66/66 [==============================] - 2s 28ms/step - loss: 0.0074 - val_loss: 2.7216e-04\n",
            "Epoch 135/1000\n",
            "66/66 [==============================] - 1s 20ms/step - loss: 0.0073 - val_loss: 2.7294e-04\n",
            "Epoch 136/1000\n",
            "66/66 [==============================] - 1s 20ms/step - loss: 0.0072 - val_loss: 2.8120e-04\n",
            "Epoch 137/1000\n",
            "66/66 [==============================] - 2s 23ms/step - loss: 0.0073 - val_loss: 2.6966e-04\n",
            "Epoch 138/1000\n",
            "66/66 [==============================] - 1s 22ms/step - loss: 0.0072 - val_loss: 2.6844e-04\n",
            "Epoch 139/1000\n",
            "66/66 [==============================] - 2s 32ms/step - loss: 0.0072 - val_loss: 2.5539e-04\n",
            "Epoch 140/1000\n",
            "66/66 [==============================] - 2s 37ms/step - loss: 0.0075 - val_loss: 2.3292e-04\n",
            "Epoch 141/1000\n",
            "66/66 [==============================] - 3s 45ms/step - loss: 0.0072 - val_loss: 2.4181e-04\n",
            "Epoch 142/1000\n",
            "66/66 [==============================] - 2s 29ms/step - loss: 0.0072 - val_loss: 2.4153e-04\n",
            "Epoch 143/1000\n",
            "66/66 [==============================] - 1s 20ms/step - loss: 0.0072 - val_loss: 2.4197e-04\n",
            "Epoch 144/1000\n",
            "66/66 [==============================] - 1s 21ms/step - loss: 0.0074 - val_loss: 2.5813e-04\n",
            "Epoch 145/1000\n",
            "66/66 [==============================] - 2s 23ms/step - loss: 0.0074 - val_loss: 2.5251e-04\n",
            "Epoch 146/1000\n",
            "66/66 [==============================] - 1s 21ms/step - loss: 0.0071 - val_loss: 2.6994e-04\n",
            "Epoch 147/1000\n",
            "66/66 [==============================] - 2s 33ms/step - loss: 0.0070 - val_loss: 2.4925e-04\n",
            "Epoch 148/1000\n",
            "66/66 [==============================] - 2s 31ms/step - loss: 0.0074 - val_loss: 2.3282e-04\n",
            "Epoch 149/1000\n",
            "66/66 [==============================] - 3s 38ms/step - loss: 0.0072 - val_loss: 2.4491e-04\n",
            "Epoch 150/1000\n",
            "66/66 [==============================] - 1s 21ms/step - loss: 0.0070 - val_loss: 2.4598e-04\n",
            "Epoch 151/1000\n",
            "66/66 [==============================] - 1s 22ms/step - loss: 0.0074 - val_loss: 2.6406e-04\n",
            "Epoch 152/1000\n",
            "66/66 [==============================] - 1s 20ms/step - loss: 0.0070 - val_loss: 2.5759e-04\n",
            "Epoch 153/1000\n",
            "66/66 [==============================] - 1s 19ms/step - loss: 0.0072 - val_loss: 2.3668e-04\n",
            "Epoch 154/1000\n",
            "66/66 [==============================] - 1s 22ms/step - loss: 0.0070 - val_loss: 2.2711e-04\n",
            "Epoch 155/1000\n",
            "66/66 [==============================] - 2s 32ms/step - loss: 0.0070 - val_loss: 2.2699e-04\n",
            "Epoch 156/1000\n",
            "66/66 [==============================] - 2s 35ms/step - loss: 0.0073 - val_loss: 2.2642e-04\n",
            "Epoch 157/1000\n",
            "66/66 [==============================] - 2s 29ms/step - loss: 0.0069 - val_loss: 2.3868e-04\n",
            "Epoch 158/1000\n",
            "66/66 [==============================] - 1s 22ms/step - loss: 0.0070 - val_loss: 2.2929e-04\n",
            "Epoch 159/1000\n",
            "66/66 [==============================] - 1s 21ms/step - loss: 0.0071 - val_loss: 2.6129e-04\n",
            "Epoch 160/1000\n",
            "66/66 [==============================] - 1s 20ms/step - loss: 0.0071 - val_loss: 2.6248e-04\n",
            "Epoch 161/1000\n",
            "66/66 [==============================] - 1s 22ms/step - loss: 0.0069 - val_loss: 2.6924e-04\n",
            "Epoch 162/1000\n",
            "66/66 [==============================] - 1s 19ms/step - loss: 0.0072 - val_loss: 2.7463e-04\n",
            "Epoch 163/1000\n",
            "66/66 [==============================] - 2s 34ms/step - loss: 0.0070 - val_loss: 2.8989e-04\n",
            "Epoch 164/1000\n",
            "66/66 [==============================] - 2s 37ms/step - loss: 0.0071 - val_loss: 2.9343e-04\n",
            "Epoch 165/1000\n",
            "66/66 [==============================] - 2s 26ms/step - loss: 0.0070 - val_loss: 3.3478e-04\n",
            "Epoch 166/1000\n",
            "66/66 [==============================] - 1s 22ms/step - loss: 0.0069 - val_loss: 2.8807e-04\n",
            "Epoch 167/1000\n",
            "66/66 [==============================] - 1s 22ms/step - loss: 0.0071 - val_loss: 2.5887e-04\n",
            "Epoch 168/1000\n",
            "66/66 [==============================] - 1s 22ms/step - loss: 0.0069 - val_loss: 2.6433e-04\n",
            "Epoch 169/1000\n",
            "66/66 [==============================] - 1s 21ms/step - loss: 0.0072 - val_loss: 2.1906e-04\n",
            "Epoch 170/1000\n",
            "66/66 [==============================] - 2s 25ms/step - loss: 0.0069 - val_loss: 2.6807e-04\n",
            "Epoch 171/1000\n",
            "66/66 [==============================] - 2s 33ms/step - loss: 0.0072 - val_loss: 2.6473e-04\n",
            "Epoch 172/1000\n",
            "66/66 [==============================] - 2s 33ms/step - loss: 0.0068 - val_loss: 2.5296e-04\n",
            "Epoch 173/1000\n",
            "66/66 [==============================] - 1s 22ms/step - loss: 0.0068 - val_loss: 2.4502e-04\n",
            "Epoch 174/1000\n",
            "66/66 [==============================] - 1s 20ms/step - loss: 0.0070 - val_loss: 2.3020e-04\n",
            "Epoch 175/1000\n",
            "66/66 [==============================] - 1s 20ms/step - loss: 0.0071 - val_loss: 2.4552e-04\n",
            "Epoch 176/1000\n",
            "66/66 [==============================] - 1s 20ms/step - loss: 0.0068 - val_loss: 2.5706e-04\n",
            "Epoch 177/1000\n",
            "66/66 [==============================] - 2s 28ms/step - loss: 0.0067 - val_loss: 2.2892e-04\n",
            "Epoch 178/1000\n",
            "66/66 [==============================] - 3s 49ms/step - loss: 0.0071 - val_loss: 2.5686e-04\n",
            "Epoch 179/1000\n",
            "66/66 [==============================] - 2s 25ms/step - loss: 0.0068 - val_loss: 2.6153e-04\n",
            "Epoch 180/1000\n",
            "66/66 [==============================] - 1s 22ms/step - loss: 0.0067 - val_loss: 2.5625e-04\n",
            "Epoch 181/1000\n",
            "66/66 [==============================] - 1s 21ms/step - loss: 0.0070 - val_loss: 2.6270e-04\n",
            "Epoch 182/1000\n",
            "66/66 [==============================] - 1s 19ms/step - loss: 0.0070 - val_loss: 2.2957e-04\n",
            "Epoch 183/1000\n",
            "66/66 [==============================] - 1s 19ms/step - loss: 0.0069 - val_loss: 2.2409e-04\n",
            "Epoch 184/1000\n",
            "66/66 [==============================] - 2s 33ms/step - loss: 0.0069 - val_loss: 2.5329e-04\n",
            "Epoch 185/1000\n",
            "66/66 [==============================] - 2s 32ms/step - loss: 0.0070 - val_loss: 2.1740e-04\n",
            "Epoch 186/1000\n",
            "66/66 [==============================] - 2s 38ms/step - loss: 0.0067 - val_loss: 2.2267e-04\n",
            "Epoch 187/1000\n",
            "66/66 [==============================] - 2s 25ms/step - loss: 0.0068 - val_loss: 2.3055e-04\n",
            "Epoch 188/1000\n",
            "66/66 [==============================] - 1s 22ms/step - loss: 0.0069 - val_loss: 2.0860e-04\n",
            "Epoch 189/1000\n",
            "66/66 [==============================] - 1s 21ms/step - loss: 0.0070 - val_loss: 2.4064e-04\n",
            "Epoch 190/1000\n",
            "66/66 [==============================] - 1s 22ms/step - loss: 0.0068 - val_loss: 2.2769e-04\n",
            "Epoch 191/1000\n",
            "66/66 [==============================] - 1s 21ms/step - loss: 0.0070 - val_loss: 2.3275e-04\n",
            "Epoch 192/1000\n",
            "66/66 [==============================] - 2s 33ms/step - loss: 0.0067 - val_loss: 2.1021e-04\n",
            "Epoch 193/1000\n",
            "66/66 [==============================] - 2s 36ms/step - loss: 0.0067 - val_loss: 2.1427e-04\n",
            "Epoch 194/1000\n",
            "66/66 [==============================] - 1s 20ms/step - loss: 0.0067 - val_loss: 2.1581e-04\n",
            "Epoch 195/1000\n",
            "66/66 [==============================] - 1s 20ms/step - loss: 0.0069 - val_loss: 2.1548e-04\n",
            "Epoch 196/1000\n",
            "66/66 [==============================] - 1s 20ms/step - loss: 0.0068 - val_loss: 2.1943e-04\n",
            "Epoch 197/1000\n",
            "66/66 [==============================] - 1s 20ms/step - loss: 0.0068 - val_loss: 2.0700e-04\n",
            "Epoch 198/1000\n",
            "66/66 [==============================] - 1s 22ms/step - loss: 0.0066 - val_loss: 2.1018e-04\n",
            "Epoch 199/1000\n",
            "66/66 [==============================] - 2s 24ms/step - loss: 0.0069 - val_loss: 2.2567e-04\n",
            "Epoch 200/1000\n",
            "66/66 [==============================] - 2s 34ms/step - loss: 0.0067 - val_loss: 2.2831e-04\n",
            "Epoch 201/1000\n",
            "66/66 [==============================] - 3s 38ms/step - loss: 0.0067 - val_loss: 2.2348e-04\n",
            "Epoch 202/1000\n",
            "66/66 [==============================] - 1s 19ms/step - loss: 0.0067 - val_loss: 2.2406e-04\n",
            "Epoch 203/1000\n",
            "66/66 [==============================] - 1s 20ms/step - loss: 0.0066 - val_loss: 2.1794e-04\n",
            "Epoch 204/1000\n",
            "66/66 [==============================] - 1s 20ms/step - loss: 0.0067 - val_loss: 2.3157e-04\n",
            "Epoch 205/1000\n",
            "66/66 [==============================] - 1s 22ms/step - loss: 0.0069 - val_loss: 2.2287e-04\n",
            "Epoch 206/1000\n",
            "66/66 [==============================] - 3s 42ms/step - loss: 0.0068 - val_loss: 2.2851e-04\n",
            "Epoch 207/1000\n",
            "66/66 [==============================] - 2s 26ms/step - loss: 0.0069 - val_loss: 2.2071e-04\n",
            "Epoch 208/1000\n",
            "66/66 [==============================] - 2s 23ms/step - loss: 0.0066 - val_loss: 2.2029e-04\n",
            "Epoch 209/1000\n",
            "66/66 [==============================] - 1s 21ms/step - loss: 0.0067 - val_loss: 2.3056e-04\n",
            "Epoch 210/1000\n",
            "66/66 [==============================] - 1s 19ms/step - loss: 0.0066 - val_loss: 2.1640e-04\n",
            "Epoch 211/1000\n",
            "66/66 [==============================] - 1s 22ms/step - loss: 0.0067 - val_loss: 2.2955e-04\n",
            "Epoch 212/1000\n",
            "66/66 [==============================] - 2s 30ms/step - loss: 0.0069 - val_loss: 2.2407e-04\n",
            "Epoch 213/1000\n",
            "66/66 [==============================] - 2s 37ms/step - loss: 0.0067 - val_loss: 2.5092e-04\n",
            "Epoch 214/1000\n",
            "66/66 [==============================] - 3s 40ms/step - loss: 0.0067 - val_loss: 2.2533e-04\n",
            "Epoch 215/1000\n",
            "66/66 [==============================] - 2s 37ms/step - loss: 0.0066 - val_loss: 2.2667e-04\n",
            "Epoch 216/1000\n",
            "66/66 [==============================] - 1s 20ms/step - loss: 0.0068 - val_loss: 2.2265e-04\n",
            "Epoch 217/1000\n",
            "66/66 [==============================] - 1s 21ms/step - loss: 0.0067 - val_loss: 2.1685e-04\n",
            "Epoch 218/1000\n",
            "66/66 [==============================] - 2s 36ms/step - loss: 0.0067 - val_loss: 2.5761e-04\n",
            "Epoch 219/1000\n",
            "66/66 [==============================] - 2s 35ms/step - loss: 0.0065 - val_loss: 2.3518e-04\n",
            "Epoch 220/1000\n",
            "66/66 [==============================] - 2s 36ms/step - loss: 0.0068 - val_loss: 2.1632e-04\n",
            "Epoch 221/1000\n",
            "66/66 [==============================] - 1s 19ms/step - loss: 0.0065 - val_loss: 2.1276e-04\n",
            "Epoch 222/1000\n",
            "66/66 [==============================] - 1s 20ms/step - loss: 0.0068 - val_loss: 2.1648e-04\n",
            "Epoch 223/1000\n",
            "66/66 [==============================] - 1s 20ms/step - loss: 0.0065 - val_loss: 2.1609e-04\n",
            "Epoch 224/1000\n",
            "66/66 [==============================] - 1s 22ms/step - loss: 0.0065 - val_loss: 2.2957e-04\n",
            "Epoch 225/1000\n",
            "66/66 [==============================] - 1s 21ms/step - loss: 0.0066 - val_loss: 2.5140e-04\n",
            "Epoch 226/1000\n",
            "66/66 [==============================] - 2s 31ms/step - loss: 0.0065 - val_loss: 2.2997e-04\n",
            "Epoch 227/1000\n",
            "66/66 [==============================] - 3s 44ms/step - loss: 0.0067 - val_loss: 2.4667e-04\n",
            "Epoch 228/1000\n",
            "66/66 [==============================] - 2s 24ms/step - loss: 0.0067 - val_loss: 2.7660e-04\n",
            "Epoch 229/1000\n",
            "66/66 [==============================] - 2s 35ms/step - loss: 0.0068 - val_loss: 2.5610e-04\n",
            "Epoch 230/1000\n",
            "66/66 [==============================] - 2s 30ms/step - loss: 0.0066 - val_loss: 2.6235e-04\n",
            "Epoch 231/1000\n",
            "66/66 [==============================] - 1s 19ms/step - loss: 0.0066 - val_loss: 2.2185e-04\n",
            "Epoch 232/1000\n",
            "66/66 [==============================] - 1s 21ms/step - loss: 0.0068 - val_loss: 2.4756e-04\n",
            "Epoch 233/1000\n",
            "66/66 [==============================] - 1s 22ms/step - loss: 0.0066 - val_loss: 2.2153e-04\n",
            "Epoch 234/1000\n",
            "66/66 [==============================] - 3s 43ms/step - loss: 0.0066 - val_loss: 2.1898e-04\n",
            "Epoch 235/1000\n",
            "66/66 [==============================] - 3s 45ms/step - loss: 0.0066 - val_loss: 2.2460e-04\n",
            "Epoch 236/1000\n",
            "66/66 [==============================] - 3s 42ms/step - loss: 0.0067 - val_loss: 2.1869e-04\n",
            "Epoch 237/1000\n",
            "66/66 [==============================] - 2s 25ms/step - loss: 0.0066 - val_loss: 2.3781e-04\n",
            "Epoch 238/1000\n",
            "66/66 [==============================] - 1s 21ms/step - loss: 0.0066 - val_loss: 2.1429e-04\n",
            "Epoch 239/1000\n",
            "66/66 [==============================] - 1s 22ms/step - loss: 0.0065 - val_loss: 2.8123e-04\n",
            "Epoch 240/1000\n",
            "66/66 [==============================] - 1s 21ms/step - loss: 0.0066 - val_loss: 2.6630e-04\n",
            "Epoch 241/1000\n",
            "66/66 [==============================] - 3s 51ms/step - loss: 0.0066 - val_loss: 2.5761e-04\n",
            "Epoch 242/1000\n",
            "66/66 [==============================] - 3s 42ms/step - loss: 0.0065 - val_loss: 2.7081e-04\n",
            "Epoch 243/1000\n",
            "66/66 [==============================] - 2s 27ms/step - loss: 0.0066 - val_loss: 2.2124e-04\n",
            "Epoch 244/1000\n",
            "66/66 [==============================] - 2s 25ms/step - loss: 0.0064 - val_loss: 2.1134e-04\n",
            "Epoch 245/1000\n",
            "66/66 [==============================] - 1s 21ms/step - loss: 0.0066 - val_loss: 2.4703e-04\n",
            "Epoch 246/1000\n",
            "66/66 [==============================] - 2s 29ms/step - loss: 0.0066 - val_loss: 2.0147e-04\n",
            "Epoch 247/1000\n",
            "66/66 [==============================] - 2s 34ms/step - loss: 0.0064 - val_loss: 2.1235e-04\n",
            "Epoch 248/1000\n",
            "66/66 [==============================] - 3s 43ms/step - loss: 0.0068 - val_loss: 2.0829e-04\n",
            "Epoch 249/1000\n",
            "66/66 [==============================] - 3s 42ms/step - loss: 0.0065 - val_loss: 2.1647e-04\n",
            "Epoch 250/1000\n",
            "66/66 [==============================] - 2s 33ms/step - loss: 0.0066 - val_loss: 2.0979e-04\n",
            "Epoch 251/1000\n",
            "66/66 [==============================] - 2s 23ms/step - loss: 0.0065 - val_loss: 2.1174e-04\n",
            "Epoch 252/1000\n",
            "66/66 [==============================] - 2s 28ms/step - loss: 0.0064 - val_loss: 2.1289e-04\n",
            "Epoch 253/1000\n",
            "66/66 [==============================] - 2s 26ms/step - loss: 0.0065 - val_loss: 2.6872e-04\n",
            "Epoch 254/1000\n",
            "66/66 [==============================] - 1s 19ms/step - loss: 0.0064 - val_loss: 2.0782e-04\n",
            "Epoch 255/1000\n",
            "66/66 [==============================] - 3s 48ms/step - loss: 0.0066 - val_loss: 2.2346e-04\n",
            "Epoch 256/1000\n",
            "66/66 [==============================] - 3s 45ms/step - loss: 0.0066 - val_loss: 2.3080e-04\n",
            "Epoch 257/1000\n",
            "66/66 [==============================] - 2s 26ms/step - loss: 0.0067 - val_loss: 2.2688e-04\n",
            "Epoch 258/1000\n",
            "66/66 [==============================] - 1s 20ms/step - loss: 0.0065 - val_loss: 2.1733e-04\n",
            "Epoch 259/1000\n",
            "66/66 [==============================] - 2s 27ms/step - loss: 0.0067 - val_loss: 2.0453e-04\n",
            "Epoch 260/1000\n",
            "66/66 [==============================] - 2s 28ms/step - loss: 0.0063 - val_loss: 2.1859e-04\n",
            "Epoch 261/1000\n",
            "66/66 [==============================] - 3s 40ms/step - loss: 0.0066 - val_loss: 2.2943e-04\n",
            "Epoch 262/1000\n",
            "66/66 [==============================] - 3s 46ms/step - loss: 0.0065 - val_loss: 2.3220e-04\n",
            "Epoch 263/1000\n",
            "66/66 [==============================] - 2s 24ms/step - loss: 0.0066 - val_loss: 2.0717e-04\n",
            "Epoch 264/1000\n",
            "66/66 [==============================] - 2s 34ms/step - loss: 0.0064 - val_loss: 2.4201e-04\n",
            "Epoch 265/1000\n",
            "66/66 [==============================] - 2s 23ms/step - loss: 0.0066 - val_loss: 2.4547e-04\n",
            "Epoch 266/1000\n",
            "66/66 [==============================] - 2s 25ms/step - loss: 0.0065 - val_loss: 2.1791e-04\n",
            "Epoch 267/1000\n",
            "66/66 [==============================] - 3s 42ms/step - loss: 0.0064 - val_loss: 2.2267e-04\n",
            "Epoch 268/1000\n",
            "66/66 [==============================] - 3s 40ms/step - loss: 0.0063 - val_loss: 2.3370e-04\n",
            "Epoch 269/1000\n",
            "66/66 [==============================] - 2s 33ms/step - loss: 0.0065 - val_loss: 2.2035e-04\n",
            "Epoch 270/1000\n",
            "66/66 [==============================] - 1s 22ms/step - loss: 0.0063 - val_loss: 2.3260e-04\n",
            "Epoch 271/1000\n",
            "66/66 [==============================] - 3s 38ms/step - loss: 0.0066 - val_loss: 2.0621e-04\n",
            "Epoch 272/1000\n",
            "66/66 [==============================] - 3s 40ms/step - loss: 0.0065 - val_loss: 2.5600e-04\n",
            "Epoch 273/1000\n",
            "66/66 [==============================] - 2s 31ms/step - loss: 0.0066 - val_loss: 2.1467e-04\n",
            "Epoch 274/1000\n",
            "66/66 [==============================] - 2s 33ms/step - loss: 0.0065 - val_loss: 2.2980e-04\n",
            "Epoch 275/1000\n",
            "66/66 [==============================] - 1s 20ms/step - loss: 0.0065 - val_loss: 3.2368e-04\n",
            "Epoch 276/1000\n",
            "66/66 [==============================] - 2s 30ms/step - loss: 0.0067 - val_loss: 2.3006e-04\n",
            "Epoch 277/1000\n",
            "66/66 [==============================] - 2s 23ms/step - loss: 0.0064 - val_loss: 2.1534e-04\n",
            "Epoch 278/1000\n",
            "66/66 [==============================] - 2s 23ms/step - loss: 0.0064 - val_loss: 2.0695e-04\n",
            "Epoch 279/1000\n",
            "66/66 [==============================] - 1s 20ms/step - loss: 0.0064 - val_loss: 2.1139e-04\n",
            "Epoch 280/1000\n",
            "66/66 [==============================] - 2s 34ms/step - loss: 0.0065 - val_loss: 2.1309e-04\n",
            "Epoch 281/1000\n",
            "66/66 [==============================] - 2s 34ms/step - loss: 0.0065 - val_loss: 2.1885e-04\n",
            "Epoch 282/1000\n",
            "66/66 [==============================] - 2s 26ms/step - loss: 0.0065 - val_loss: 2.0813e-04\n",
            "Epoch 283/1000\n",
            "66/66 [==============================] - 1s 21ms/step - loss: 0.0065 - val_loss: 2.5632e-04\n",
            "Epoch 284/1000\n",
            "66/66 [==============================] - 2s 26ms/step - loss: 0.0064 - val_loss: 2.5548e-04\n",
            "Epoch 285/1000\n",
            "66/66 [==============================] - 2s 28ms/step - loss: 0.0065 - val_loss: 2.2174e-04\n",
            "Epoch 286/1000\n",
            "66/66 [==============================] - 2s 35ms/step - loss: 0.0065 - val_loss: 2.1238e-04\n",
            "Epoch 287/1000\n",
            "66/66 [==============================] - 1s 22ms/step - loss: 0.0064 - val_loss: 3.1701e-04\n",
            "Epoch 288/1000\n",
            "66/66 [==============================] - 2s 31ms/step - loss: 0.0064 - val_loss: 2.0962e-04\n",
            "Epoch 289/1000\n",
            "66/66 [==============================] - 2s 32ms/step - loss: 0.0066 - val_loss: 3.0628e-04\n",
            "Epoch 290/1000\n",
            "66/66 [==============================] - 2s 30ms/step - loss: 0.0064 - val_loss: 2.2975e-04\n",
            "Epoch 291/1000\n",
            "66/66 [==============================] - 2s 30ms/step - loss: 0.0066 - val_loss: 2.5417e-04\n",
            "Epoch 292/1000\n",
            "66/66 [==============================] - 2s 27ms/step - loss: 0.0066 - val_loss: 2.2456e-04\n",
            "Epoch 293/1000\n",
            "66/66 [==============================] - 1s 20ms/step - loss: 0.0063 - val_loss: 2.1536e-04\n",
            "Epoch 294/1000\n",
            "66/66 [==============================] - 2s 23ms/step - loss: 0.0064 - val_loss: 2.2623e-04\n",
            "Epoch 295/1000\n",
            "66/66 [==============================] - 3s 40ms/step - loss: 0.0064 - val_loss: 2.8372e-04\n",
            "Epoch 296/1000\n",
            "66/66 [==============================] - 2s 32ms/step - loss: 0.0063 - val_loss: 2.5397e-04\n",
            "Epoch 297/1000\n",
            "66/66 [==============================] - 2s 31ms/step - loss: 0.0066 - val_loss: 2.7646e-04\n",
            "Epoch 298/1000\n",
            "66/66 [==============================] - 3s 43ms/step - loss: 0.0065 - val_loss: 2.4166e-04\n",
            "Epoch 299/1000\n",
            "66/66 [==============================] - 2s 36ms/step - loss: 0.0063 - val_loss: 2.2864e-04\n",
            "Epoch 300/1000\n",
            "66/66 [==============================] - 3s 39ms/step - loss: 0.0065 - val_loss: 2.2110e-04\n",
            "Epoch 301/1000\n",
            "66/66 [==============================] - 3s 40ms/step - loss: 0.0063 - val_loss: 2.1327e-04\n",
            "Epoch 302/1000\n",
            "66/66 [==============================] - 2s 23ms/step - loss: 0.0066 - val_loss: 2.2847e-04\n",
            "Epoch 303/1000\n",
            "66/66 [==============================] - 2s 37ms/step - loss: 0.0065 - val_loss: 2.2458e-04\n",
            "Epoch 304/1000\n",
            "66/66 [==============================] - 1s 22ms/step - loss: 0.0066 - val_loss: 2.8011e-04\n",
            "Epoch 305/1000\n",
            "66/66 [==============================] - 1s 20ms/step - loss: 0.0065 - val_loss: 2.0575e-04\n",
            "Epoch 306/1000\n",
            "66/66 [==============================] - 2s 31ms/step - loss: 0.0064 - val_loss: 2.2166e-04\n",
            "Epoch 307/1000\n",
            "66/66 [==============================] - 2s 34ms/step - loss: 0.0065 - val_loss: 2.2210e-04\n",
            "Epoch 308/1000\n",
            "66/66 [==============================] - 2s 35ms/step - loss: 0.0063 - val_loss: 2.2306e-04\n",
            "Epoch 309/1000\n",
            "66/66 [==============================] - 2s 25ms/step - loss: 0.0062 - val_loss: 2.3169e-04\n",
            "Epoch 310/1000\n",
            "66/66 [==============================] - 2s 32ms/step - loss: 0.0066 - val_loss: 2.0561e-04\n",
            "Epoch 311/1000\n",
            "66/66 [==============================] - 2s 28ms/step - loss: 0.0064 - val_loss: 2.7551e-04\n",
            "Epoch 312/1000\n",
            "66/66 [==============================] - 2s 23ms/step - loss: 0.0066 - val_loss: 2.0499e-04\n",
            "Epoch 313/1000\n",
            "66/66 [==============================] - 1s 20ms/step - loss: 0.0065 - val_loss: 2.5509e-04\n",
            "Epoch 314/1000\n",
            "66/66 [==============================] - 2s 28ms/step - loss: 0.0065 - val_loss: 2.2075e-04\n",
            "Epoch 315/1000\n",
            "66/66 [==============================] - 3s 49ms/step - loss: 0.0063 - val_loss: 2.7237e-04\n",
            "Epoch 316/1000\n",
            "66/66 [==============================] - 2s 28ms/step - loss: 0.0066 - val_loss: 2.1335e-04\n",
            "Epoch 317/1000\n",
            "66/66 [==============================] - 1s 21ms/step - loss: 0.0063 - val_loss: 2.2405e-04\n",
            "Epoch 318/1000\n",
            "66/66 [==============================] - 2s 23ms/step - loss: 0.0064 - val_loss: 2.2393e-04\n",
            "Epoch 319/1000\n",
            "66/66 [==============================] - 1s 21ms/step - loss: 0.0063 - val_loss: 2.1710e-04\n",
            "Epoch 320/1000\n",
            "66/66 [==============================] - 1s 21ms/step - loss: 0.0066 - val_loss: 2.1412e-04\n",
            "Epoch 321/1000\n",
            "66/66 [==============================] - 1s 20ms/step - loss: 0.0065 - val_loss: 2.3253e-04\n",
            "Epoch 322/1000\n",
            "66/66 [==============================] - 2s 28ms/step - loss: 0.0064 - val_loss: 2.4820e-04\n",
            "Epoch 323/1000\n",
            "66/66 [==============================] - 2s 36ms/step - loss: 0.0064 - val_loss: 2.2720e-04\n",
            "Epoch 324/1000\n",
            "66/66 [==============================] - 2s 33ms/step - loss: 0.0064 - val_loss: 2.0915e-04\n",
            "Epoch 325/1000\n",
            "66/66 [==============================] - 2s 26ms/step - loss: 0.0064 - val_loss: 2.0796e-04\n",
            "Epoch 326/1000\n",
            "66/66 [==============================] - 1s 20ms/step - loss: 0.0063 - val_loss: 2.7183e-04\n",
            "Epoch 327/1000\n",
            "66/66 [==============================] - 1s 21ms/step - loss: 0.0063 - val_loss: 2.2791e-04\n",
            "Epoch 328/1000\n",
            "66/66 [==============================] - 1s 20ms/step - loss: 0.0064 - val_loss: 2.1761e-04\n",
            "Epoch 329/1000\n",
            "66/66 [==============================] - 1s 20ms/step - loss: 0.0065 - val_loss: 2.5304e-04\n",
            "Epoch 330/1000\n",
            "66/66 [==============================] - 1s 22ms/step - loss: 0.0063 - val_loss: 2.1200e-04\n",
            "Epoch 331/1000\n",
            "66/66 [==============================] - 2s 34ms/step - loss: 0.0062 - val_loss: 2.0769e-04\n",
            "Epoch 332/1000\n",
            "66/66 [==============================] - 2s 31ms/step - loss: 0.0065 - val_loss: 2.3923e-04\n",
            "Epoch 333/1000\n",
            "66/66 [==============================] - 2s 32ms/step - loss: 0.0064 - val_loss: 2.4872e-04\n",
            "Epoch 334/1000\n",
            "66/66 [==============================] - 2s 23ms/step - loss: 0.0064 - val_loss: 2.0326e-04\n",
            "Epoch 335/1000\n",
            "66/66 [==============================] - 1s 20ms/step - loss: 0.0065 - val_loss: 2.3949e-04\n",
            "Epoch 336/1000\n",
            "66/66 [==============================] - 1s 21ms/step - loss: 0.0064 - val_loss: 2.6381e-04\n",
            "Epoch 337/1000\n",
            "66/66 [==============================] - 1s 20ms/step - loss: 0.0063 - val_loss: 2.1525e-04\n",
            "Epoch 338/1000\n",
            "66/66 [==============================] - 2s 23ms/step - loss: 0.0063 - val_loss: 2.0780e-04\n",
            "Epoch 339/1000\n",
            "66/66 [==============================] - 1s 20ms/step - loss: 0.0065 - val_loss: 2.3071e-04\n",
            "Epoch 340/1000\n",
            "66/66 [==============================] - 2s 26ms/step - loss: 0.0065 - val_loss: 2.5204e-04\n",
            "Epoch 341/1000\n",
            "66/66 [==============================] - 2s 35ms/step - loss: 0.0063 - val_loss: 2.1130e-04\n",
            "Epoch 342/1000\n",
            "66/66 [==============================] - 2s 37ms/step - loss: 0.0063 - val_loss: 2.2167e-04\n",
            "Epoch 343/1000\n",
            "66/66 [==============================] - 2s 23ms/step - loss: 0.0062 - val_loss: 2.8561e-04\n",
            "Epoch 344/1000\n",
            "66/66 [==============================] - 1s 20ms/step - loss: 0.0064 - val_loss: 2.3610e-04\n",
            "Epoch 345/1000\n",
            "66/66 [==============================] - 2s 23ms/step - loss: 0.0064 - val_loss: 2.3549e-04\n",
            "Epoch 346/1000\n",
            "66/66 [==============================] - 1s 20ms/step - loss: 0.0063 - val_loss: 2.1548e-04\n",
            "Epoch 347/1000\n",
            "66/66 [==============================] - 1s 21ms/step - loss: 0.0064 - val_loss: 2.2002e-04\n",
            "Epoch 348/1000\n",
            "66/66 [==============================] - 2s 32ms/step - loss: 0.0064 - val_loss: 4.1616e-04\n",
            "Epoch 349/1000\n",
            "66/66 [==============================] - 2s 35ms/step - loss: 0.0064 - val_loss: 2.1236e-04\n",
            "Epoch 350/1000\n",
            "66/66 [==============================] - 2s 30ms/step - loss: 0.0065 - val_loss: 3.3269e-04\n",
            "Epoch 351/1000\n",
            "66/66 [==============================] - 2s 23ms/step - loss: 0.0063 - val_loss: 2.6600e-04\n",
            "Epoch 352/1000\n",
            "66/66 [==============================] - 1s 22ms/step - loss: 0.0064 - val_loss: 2.3543e-04\n",
            "Epoch 353/1000\n",
            "66/66 [==============================] - 1s 20ms/step - loss: 0.0063 - val_loss: 2.5743e-04\n",
            "Epoch 354/1000\n",
            "66/66 [==============================] - 1s 19ms/step - loss: 0.0064 - val_loss: 2.4114e-04\n",
            "Epoch 355/1000\n",
            "66/66 [==============================] - 2s 26ms/step - loss: 0.0064 - val_loss: 2.5076e-04\n",
            "Epoch 356/1000\n",
            "66/66 [==============================] - 2s 33ms/step - loss: 0.0063 - val_loss: 2.2725e-04\n",
            "Epoch 357/1000\n",
            "66/66 [==============================] - 2s 37ms/step - loss: 0.0063 - val_loss: 2.2127e-04\n",
            "Epoch 358/1000\n",
            "66/66 [==============================] - 2s 25ms/step - loss: 0.0065 - val_loss: 2.4867e-04\n",
            "Epoch 359/1000\n",
            "66/66 [==============================] - 1s 21ms/step - loss: 0.0063 - val_loss: 2.2208e-04\n",
            "Epoch 360/1000\n",
            "66/66 [==============================] - 1s 21ms/step - loss: 0.0064 - val_loss: 2.6583e-04\n",
            "Epoch 361/1000\n",
            "66/66 [==============================] - 2s 24ms/step - loss: 0.0064 - val_loss: 2.3792e-04\n",
            "Epoch 362/1000\n",
            "66/66 [==============================] - 1s 22ms/step - loss: 0.0064 - val_loss: 2.7874e-04\n",
            "Epoch 363/1000\n",
            "66/66 [==============================] - 2s 30ms/step - loss: 0.0064 - val_loss: 3.0533e-04\n",
            "Epoch 364/1000\n",
            "66/66 [==============================] - 3s 39ms/step - loss: 0.0064 - val_loss: 2.9877e-04\n",
            "Epoch 365/1000\n",
            "66/66 [==============================] - 1s 21ms/step - loss: 0.0063 - val_loss: 2.6639e-04\n",
            "Epoch 366/1000\n",
            "66/66 [==============================] - 1s 22ms/step - loss: 0.0063 - val_loss: 2.6908e-04\n",
            "Epoch 367/1000\n",
            "66/66 [==============================] - 1s 21ms/step - loss: 0.0064 - val_loss: 2.7565e-04\n",
            "Epoch 368/1000\n",
            "66/66 [==============================] - 1s 21ms/step - loss: 0.0064 - val_loss: 2.0302e-04\n",
            "Epoch 369/1000\n",
            "66/66 [==============================] - 2s 28ms/step - loss: 0.0063 - val_loss: 3.3980e-04\n",
            "Epoch 370/1000\n",
            "66/66 [==============================] - 2s 36ms/step - loss: 0.0064 - val_loss: 3.3632e-04\n",
            "Epoch 371/1000\n",
            "66/66 [==============================] - 2s 36ms/step - loss: 0.0064 - val_loss: 2.2014e-04\n"
          ]
        }
      ],
      "source": [
        "tf.keras.utils.set_random_seed(42)\n",
        "\n",
        "model = LSTM_model()\n",
        "epochs = 1000\n",
        "early_stopping = tf.keras.callbacks.EarlyStopping(monitor = 'val_loss', patience=125, restore_best_weights=True)\n",
        "learning_rate = 0.0001\n",
        "optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate)\n",
        "\n",
        "model.compile(loss='mean_squared_error', optimizer=optimizer)\n",
        "\n",
        "history = model.fit(\n",
        "    train_set,\n",
        "    epochs=epochs,\n",
        "    validation_data=val_set,\n",
        "    shuffle=False,\n",
        "    callbacks=[early_stopping],\n",
        "    verbose=1\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5b5aa6ff",
      "metadata": {
        "id": "5b5aa6ff"
      },
      "source": [
        "Okay! Our model is trained and `EarlyStopping` activated around 400 epochs, saving extra 600 epochs.\n",
        "\n",
        "Let's plot the training and validation loss."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2385824a",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 480
        },
        "id": "2385824a",
        "outputId": "5b6e6837-2052-4218-9ea3-f221874c3d4f"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "train_loss = model.history.history['loss']\n",
        "val_loss = model.history.history['val_loss']\n",
        "epochs = range(1, len(train_loss) + 1)\n",
        "\n",
        "plt.plot(epochs, train_loss, 'b', label='Training Loss')\n",
        "plt.plot(epochs, val_loss, 'r', label='Validation Loss')\n",
        "plt.title('Training and Validation Loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a80328aa",
      "metadata": {
        "id": "a80328aa"
      },
      "source": [
        "Now use this trained model to predict the oil production on test set"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a549d510",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a549d510",
        "outputId": "316825db-2169-4d5a-ac90-b045d388b52a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "14/14 [==============================] - 1s 8ms/step\n"
          ]
        }
      ],
      "source": [
        "predictions = model.predict(test_set)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7523bb4f",
      "metadata": {
        "id": "7523bb4f"
      },
      "source": [
        "The below code concatenates the `X_test` with predicted data, reverses the scaling, and adds the `PREDICTED_OIL` column to the data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ba016fb3",
      "metadata": {
        "id": "ba016fb3",
        "scrolled": false
      },
      "outputs": [],
      "source": [
        "df_pred = pd.concat([pd.DataFrame(X_test[:,1:][window_size:]),pd.DataFrame(predictions)], axis = 1)\n",
        "rev_trans = scaler.inverse_transform(df_pred)\n",
        "df_final = data.iloc[-predictions.shape[0]:].copy()\n",
        "df_final['PREDICTED_OIL'] = rev_trans[:,-1]"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f63516b6",
      "metadata": {
        "id": "f63516b6"
      },
      "source": [
        "Let's plot the predicted and real data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "711908b1",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "711908b1",
        "outputId": "f276f698-4d97-48c2-af1c-bab976c1ca52"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.set(style=\"whitegrid\")\n",
        "plt.figure(figsize=(12, 6))\n",
        "sns.lineplot(data=df_final, x='DATEPRD', y='BORE_OIL_VOL', label='Actual', errorbar=None)\n",
        "sns.lineplot(data=df_final, x='DATEPRD', y='PREDICTED_OIL', label='Predicted', errorbar=None)\n",
        "plt.xlabel('Date')\n",
        "plt.ylabel('Oil Production')\n",
        "plt.title('Actual vs. Predicted')\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d5e126e6",
      "metadata": {
        "id": "d5e126e6"
      },
      "source": [
        "Bingo! Excellent match.\n",
        "\n",
        "Now create a DataFrame named `compare_data` having columns `DATEPRD`, `BORE_OIL_VOL`, and `PREDICTED_OIL` to compare actual and predicted data values side by side."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7bde423e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "7bde423e",
        "outputId": "00a4634d-cd9c-4d56-85c3-c06db91ea0a5"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-5a6af700-e34a-4ddf-bbd7-729ff8b78d08\" class=\"colab-df-container\">\n",
              "    <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>DATEPRD</th>\n",
              "      <th>BORE_OIL_VOL</th>\n",
              "      <th>PREDICTED_OIL</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>4523</th>\n",
              "      <td>2015-07-02</td>\n",
              "      <td>726.33</td>\n",
              "      <td>700.656111</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4524</th>\n",
              "      <td>2015-07-03</td>\n",
              "      <td>723.42</td>\n",
              "      <td>706.033942</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4525</th>\n",
              "      <td>2015-07-04</td>\n",
              "      <td>711.93</td>\n",
              "      <td>706.854389</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4526</th>\n",
              "      <td>2015-07-05</td>\n",
              "      <td>707.58</td>\n",
              "      <td>708.506076</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4527</th>\n",
              "      <td>2015-07-06</td>\n",
              "      <td>701.27</td>\n",
              "      <td>708.432104</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4962</th>\n",
              "      <td>2016-09-13</td>\n",
              "      <td>0.00</td>\n",
              "      <td>48.810617</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4963</th>\n",
              "      <td>2016-09-14</td>\n",
              "      <td>0.00</td>\n",
              "      <td>48.821585</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4964</th>\n",
              "      <td>2016-09-15</td>\n",
              "      <td>0.00</td>\n",
              "      <td>48.830886</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4965</th>\n",
              "      <td>2016-09-16</td>\n",
              "      <td>0.00</td>\n",
              "      <td>48.833958</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4966</th>\n",
              "      <td>2016-09-17</td>\n",
              "      <td>0.00</td>\n",
              "      <td>48.822024</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>444 rows × 3 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5a6af700-e34a-4ddf-bbd7-729ff8b78d08')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-5a6af700-e34a-4ddf-bbd7-729ff8b78d08 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-5a6af700-e34a-4ddf-bbd7-729ff8b78d08');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-de53126c-a968-4171-b841-2944aed35423\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-de53126c-a968-4171-b841-2944aed35423')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-de53126c-a968-4171-b841-2944aed35423 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "        DATEPRD  BORE_OIL_VOL  PREDICTED_OIL\n",
              "4523 2015-07-02        726.33     700.656111\n",
              "4524 2015-07-03        723.42     706.033942\n",
              "4525 2015-07-04        711.93     706.854389\n",
              "4526 2015-07-05        707.58     708.506076\n",
              "4527 2015-07-06        701.27     708.432104\n",
              "...         ...           ...            ...\n",
              "4962 2016-09-13          0.00      48.810617\n",
              "4963 2016-09-14          0.00      48.821585\n",
              "4964 2016-09-15          0.00      48.830886\n",
              "4965 2016-09-16          0.00      48.833958\n",
              "4966 2016-09-17          0.00      48.822024\n",
              "\n",
              "[444 rows x 3 columns]"
            ]
          },
          "execution_count": 35,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "compare_data = df_final[['DATEPRD', 'BORE_OIL_VOL', 'PREDICTED_OIL']]\n",
        "compare_data"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "yYyO0S7uMLf6",
      "metadata": {
        "id": "yYyO0S7uMLf6"
      },
      "source": [
        "I trained two separate models for gas and water and here is the result of the blind dataset:"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2b07d9eb",
      "metadata": {
        "id": "2b07d9eb"
      },
      "source": [
        "![gas_model](gas_model.png)\n",
        "![water_model](water_model.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9c04a8ae",
      "metadata": {
        "id": "9c04a8ae"
      },
      "source": [
        "Now it's your turn. Please try to train your own model, but instead of using only one well data, use the full set of data. Try hyperparameter tunings, like more epochs, layers, etc., to have good match.  \n",
        "\n",
        "Best of luck. . ."
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.13"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}