demo_starting_python_E&P_1hour.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "demo_starting_python_E&P_1hour.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "1bzdzcjfAuEs"
      ],
      "authorship_tag": "ABX9TyPTW4KjYtgIqO4nlk5pRCX4",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/yohanesnuwara/python-bootcamp-for-geoengineers/blob/master/demo_starting_python_E%26P_1hour.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u1NwL3fQ_-PS"
      },
      "source": [
        "# Demo: Starting Python for Exploration and Production in 1 Hour\n",
        "\n",
        "**Created by:** *Yohanes Nuwara*\n",
        "\n",
        "This notebook is used for personal demonstration in my technical talk and knowledge sharing sessions. \n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_2IZDnx5BVap"
      },
      "source": [
        "## Demo 0: Kickstarting\n",
        "\n",
        "This session demonstrate how to use Google Colab (switching from CPU to GPU vice versa, connecting to runtime, scripting, and running the cell)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dmRoI54fbH12",
        "outputId": "a52c409f-cbcf-4fe6-d549-0d9d7679a169",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print('hello world')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "hello world\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EAlK4Y6CbPLh",
        "outputId": "26e1d4be-9e6f-44de-a568-1926815299d7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "a = 10\n",
        "\n",
        "if a > 5:\n",
        "  b = a**2\n",
        "  print(b)\n",
        "if a <= 5:\n",
        "  b = 1 / a\n",
        "  print(b)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1bzdzcjfAuEs"
      },
      "source": [
        "## Demo 1: Python and its Effectiveness\n",
        "\n",
        "This session demonstrate how to apply list comprehension to solve problems in engineering fast, easily, and effectively i.e. to reduce the lines of codes. \n",
        "\n",
        "In the example below, we want to compute the difference between two consecutive elements in an array"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FiQvGV7xX3Od"
      },
      "source": [
        "# create array\n",
        "array = [13, 56, 78, 23, 45, 67, 82, 34, 25, 66, 23, 12, 45, 56, 78, 89]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GmNtQpP1X5pf",
        "outputId": "951cff86-08c4-4e3b-ecde-a06a1995c96c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# NOT using LIST COMPREHENSION\n",
        "difference = []\n",
        "\n",
        "for i, j in zip(range(0, len(array)-1), range(1, len(array))):\n",
        "  diff = array[j] - array[i]\n",
        "  # append result of each iter to the blank array\n",
        "  difference.append(diff)\n",
        "\n",
        "print(difference)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[43, 22, -55, 22, 22, 15, -48, -9, 41, -43, -11, 33, 11, 22, 11]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VhAIIw5LYC9B",
        "outputId": "396bdeea-dc1a-4642-b4d1-4a47755f186d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# using LIST COMPREHENSION\n",
        "difference = [j-i for i, j in zip(array[:-1], array[1:])]\n",
        "print(difference)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[43, 22, -55, 22, 22, 15, -48, -9, 41, -43, -11, 33, 11, 22, 11]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K-O7IZY1bQ1x"
      },
      "source": [
        "## Demo 2: Numpy, Matplotlib, and Pandas\n",
        "\n",
        "This session demonstrate how to use Numpy, Matplotlib, and Pandas libraries, and to guarantee that if you master these libraries, you will master how to solve engineering problems with Python. \n",
        "\n",
        "In the example below, we use Numpy to create array of x and y trigonometric function, Matplotlib to visualize the function, and Pandas to convert the data into a dataframe. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NbWHHnNcb1GO"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fDsksszCcAvh"
      },
      "source": [
        "# Numpy\n",
        "\n",
        "## create x array\n",
        "x = np.arange(0, 370, 10)\n",
        "\n",
        "## compute sine and cosine from x array\n",
        "ysin = np.sin(np.deg2rad(x))\n",
        "ycos = np.cos(np.deg2rad(x))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lo4H8l6jdimi",
        "outputId": "30841425-8d4a-4a6f-b461-90a1d4c526cc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        }
      },
      "source": [
        "# Matplotlib\n",
        "\n",
        "## resize the plot\n",
        "plt.figure(figsize=(10,5))\n",
        "\n",
        "## plot, specify color, linetype, linewidth, and give labels \n",
        "plt.plot(x, ysin, '.-', color='purple', label='y=sin(x)')\n",
        "plt.plot(x, ycos, '*-', color='green', linewidth=1, label='y=cos(x)')\n",
        "\n",
        "## give title, with size and pad\n",
        "plt.title('Sine and Cosine Function', size=20, pad=10)\n",
        "\n",
        "## give labels, with size\n",
        "plt.xlabel('x', size=15)\n",
        "plt.ylabel('y', size=15)\n",
        "\n",
        "## limit the axes\n",
        "plt.xlim(0, 360)\n",
        "\n",
        "## show the legends and specify its location in the plot\n",
        "plt.legend(loc='upper center')\n",
        "\n",
        "## show the grids\n",
        "plt.grid()\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7XSNKsTIeG6g",
        "outputId": "39e1fb75-c44f-4e5d-867b-80954d62f73e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        }
      },
      "source": [
        "# Pandas\n",
        "\n",
        "## create dataframe\n",
        "df = pd.DataFrame({'x': x, 'sin(x)': ysin, 'cos(x)': ycos})\n",
        "\n",
        "## display the first 10 rows\n",
        "df.head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>x</th>\n",
              "      <th>sin(x)</th>\n",
              "      <th>cos(x)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10</td>\n",
              "      <td>0.173648</td>\n",
              "      <td>9.848078e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>20</td>\n",
              "      <td>0.342020</td>\n",
              "      <td>9.396926e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>30</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>8.660254e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>40</td>\n",
              "      <td>0.642788</td>\n",
              "      <td>7.660444e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>50</td>\n",
              "      <td>0.766044</td>\n",
              "      <td>6.427876e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>60</td>\n",
              "      <td>0.866025</td>\n",
              "      <td>5.000000e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>70</td>\n",
              "      <td>0.939693</td>\n",
              "      <td>3.420201e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>80</td>\n",
              "      <td>0.984808</td>\n",
              "      <td>1.736482e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>90</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>6.123234e-17</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    x    sin(x)        cos(x)\n",
              "0   0  0.000000  1.000000e+00\n",
              "1  10  0.173648  9.848078e-01\n",
              "2  20  0.342020  9.396926e-01\n",
              "3  30  0.500000  8.660254e-01\n",
              "4  40  0.642788  7.660444e-01\n",
              "5  50  0.766044  6.427876e-01\n",
              "6  60  0.866025  5.000000e-01\n",
              "7  70  0.939693  3.420201e-01\n",
              "8  80  0.984808  1.736482e-01\n",
              "9  90  1.000000  6.123234e-17"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DqQJTV2QckCQ"
      },
      "source": [
        "## Demo 3: Access Exploration Open Dataset\n",
        "\n",
        "This session demonstrates how to stream EXPLORATION open dataset from a website directly to Google Colab (without downloading to our PC) and open the data quickly. \n",
        "\n",
        "In the example below, we access a well-log dataset from the KGS web data repository, use Lasio to read the data, and display it. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U7w5lKDDhYQG",
        "outputId": "cf8d3bb4-0d2d-4d24-efdf-c5caf4fa2cec",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 207
        }
      },
      "source": [
        "# get dataset from the open repo website (e.g. KGS)\n",
        "!wget 'http://www.kgs.ku.edu/PRS/Scans/Log_Summary/2020.zip'"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-10-15 10:03:39--  http://www.kgs.ku.edu/PRS/Scans/Log_Summary/2020.zip\n",
            "Resolving www.kgs.ku.edu (www.kgs.ku.edu)... 129.237.140.42\n",
            "Connecting to www.kgs.ku.edu (www.kgs.ku.edu)|129.237.140.42|:80... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 107287088 (102M) [application/zip]\n",
            "Saving to: ‘2020.zip’\n",
            "\n",
            "2020.zip            100%[===================>] 102.32M  11.0MB/s    in 11s     \n",
            "\n",
            "2020-10-15 10:03:50 (9.37 MB/s) - ‘2020.zip’ saved [107287088/107287088]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BQNv3d61h5UM"
      },
      "source": [
        "# unzip file and store to directory \"KGS\"\n",
        "!unzip '/content/2020.zip' -d '/content/KGS'\n",
        "!unzip '/content/KGS/1051325649.zip' -d '/content/KGS/logs'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YvOaiMu7jg8O",
        "outputId": "a7a95ac0-4cda-485c-c08c-9b6a9b37614a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 124
        }
      },
      "source": [
        "# install Lasio library to read well log data\n",
        "!pip install lasio"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting lasio\n",
            "  Downloading https://files.pythonhosted.org/packages/5e/8e/ce58a22ec8454a12f92333a50f2add5f6131218c4815952d6ca7cbd578f0/lasio-0.28-py3-none-any.whl\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from lasio) (1.18.5)\n",
            "Installing collected packages: lasio\n",
            "Successfully installed lasio-0.28\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R6N5pD09jri8"
      },
      "source": [
        "# use Lasio\n",
        "import lasio\n",
        "\n",
        "# read well log data\n",
        "well = lasio.read('/content/KGS/logs/1051325649.las')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pt5QWniglBuz",
        "outputId": "da077424-10dc-41bd-d6c8-5f208a5e6207",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 519
        }
      },
      "source": [
        "plt.figure(figsize=(3,8)) # chart size\n",
        "\n",
        "plt.plot(well['NPHI'], well['DEPT'], color='blue') # plot the data\n",
        "plt.title('Neutron Porosity Log', size=15, pad=10)\n",
        "plt.xlabel(\"NPHI (v/v)\"); plt.ylabel(\"Depth (m)\") # labels\n",
        "plt.xlim(0, 0.45)\n",
        "plt.grid(True) # give chart grids\n",
        "plt.gca().invert_yaxis() # invert y-axis\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 216x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z9K98xR1nWd1"
      },
      "source": [
        "## Demo 4: Access Production Open Dataset\n",
        "\n",
        "This session demonstrates how to stream PRODUCTION open dataset from a website directly to Google Colab (without downloading to our PC) and open the data quickly.\n",
        "\n",
        "In the example below, we access a production history data of the Volve field in North Sea from an available database in Zenodo (Alfonso Reyes), and display the production plot. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_3e_26m9na8T",
        "outputId": "3e010ffa-59c0-4981-b018-01f88472c6fc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 207
        }
      },
      "source": [
        "# get the dataset from the open repo website (e.g. Volve Zenodo repo)\n",
        "!wget 'https://zenodo.org/record/2596620/files/f0nzie/volve_eclipse_reservoir-v0.1.zip'"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-10-05 05:54:16--  https://zenodo.org/record/2596620/files/f0nzie/volve_eclipse_reservoir-v0.1.zip\n",
            "Resolving zenodo.org (zenodo.org)... 137.138.76.77\n",
            "Connecting to zenodo.org (zenodo.org)|137.138.76.77|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 29500106 (28M) [application/octet-stream]\n",
            "Saving to: ‘volve_eclipse_reservoir-v0.1.zip’\n",
            "\n",
            "volve_eclipse_reser 100%[===================>]  28.13M  79.4MB/s    in 0.4s    \n",
            "\n",
            "2020-10-05 05:54:17 (79.4 MB/s) - ‘volve_eclipse_reservoir-v0.1.zip’ saved [29500106/29500106]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Km9QZaFGn8QF"
      },
      "source": [
        "# unzip the zip file\n",
        "!unzip '/content/volve_eclipse_reservoir-v0.1.zip' -d '/content/Volve'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UHecglrgoRLl",
        "outputId": "1bd5fd50-bf7d-413d-f3f4-8ca9fdfcfe02",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        }
      },
      "source": [
        "# define the filepath of production data in Excel file\n",
        "filepath = '/content/Volve/f0nzie-volve_eclipse_reservoir-413a669/inst/rawdata/Volve production data.xlsx'\n",
        "\n",
        "# read excel from the above filepath\n",
        "df = pd.read_excel(filepath, sheet_name='Monthly Production Data')\n",
        "\n",
        "# output the last 10 rows of the dataframe\n",
        "df.head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Wellbore name</th>\n",
              "      <th>NPDCode</th>\n",
              "      <th>Year</th>\n",
              "      <th>Month</th>\n",
              "      <th>On Stream</th>\n",
              "      <th>Oil</th>\n",
              "      <th>Gas</th>\n",
              "      <th>Water</th>\n",
              "      <th>GI</th>\n",
              "      <th>WI</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>15/9-F-4</td>\n",
              "      <td>5693.0</td>\n",
              "      <td>2007.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>15/9-F-5</td>\n",
              "      <td>5769.0</td>\n",
              "      <td>2007.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>15/9-F-4</td>\n",
              "      <td>5693.0</td>\n",
              "      <td>2007.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>15/9-F-5</td>\n",
              "      <td>5769.0</td>\n",
              "      <td>2007.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>15/9-F-4</td>\n",
              "      <td>5693.0</td>\n",
              "      <td>2007.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>15/9-F-5</td>\n",
              "      <td>5769.0</td>\n",
              "      <td>2007.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>15/9-F-4</td>\n",
              "      <td>5693.0</td>\n",
              "      <td>2007.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>15/9-F-5</td>\n",
              "      <td>5769.0</td>\n",
              "      <td>2007.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>15/9-F-4</td>\n",
              "      <td>5693.0</td>\n",
              "      <td>2008.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>15/9-F-5</td>\n",
              "      <td>5769.0</td>\n",
              "      <td>2008.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Wellbore name  NPDCode    Year  Month On Stream  Oil  Gas Water   GI   WI\n",
              "0      15/9-F-4   5693.0  2007.0    9.0       NaN  NaN  NaN   NaN  NaN  NaN\n",
              "1      15/9-F-5   5769.0  2007.0    9.0       NaN  NaN  NaN   NaN  NaN  NaN\n",
              "2      15/9-F-4   5693.0  2007.0   10.0       NaN  NaN  NaN   NaN  NaN  NaN\n",
              "3      15/9-F-5   5769.0  2007.0   10.0       NaN  NaN  NaN   NaN  NaN  NaN\n",
              "4      15/9-F-4   5693.0  2007.0   11.0       NaN  NaN  NaN   NaN  NaN  NaN\n",
              "5      15/9-F-5   5769.0  2007.0   11.0       NaN  NaN  NaN   NaN  NaN  NaN\n",
              "6      15/9-F-4   5693.0  2007.0   12.0       NaN  NaN  NaN   NaN  NaN  NaN\n",
              "7      15/9-F-5   5769.0  2007.0   12.0       NaN  NaN  NaN   NaN  NaN  NaN\n",
              "8      15/9-F-4   5693.0  2008.0    1.0         0  NaN  NaN   NaN  NaN  NaN\n",
              "9      15/9-F-5   5769.0  2008.0    1.0         0  NaN  NaN   NaN  NaN  NaN"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QYl4zWcYp7XX",
        "outputId": "d520076c-cc26-45f4-abd8-8d040865ef9e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 416
        }
      },
      "source": [
        "# select only data of well 15/9-F-12\n",
        "well_prod = df[df['Wellbore name']=='15/9-F-12']\n",
        "\n",
        "# make array of months\n",
        "t = np.arange(len(well_prod))\n",
        "\n",
        "# specify row contains oil cum. prod.\n",
        "Np = well_prod['Oil']\n",
        "\n",
        "# plot oil cum. prod vs month\n",
        "plt.figure(figsize=(12,6))\n",
        "\n",
        "plt.step(t, Np, label='Well 15/9-F-12 (Volve Field)')\n",
        "plt.title('Oil Cumulative Production over Months Since February 2008', size=20, pad=10)\n",
        "plt.xlim(min(t), max(t)); plt.ylim(ymin=0)\n",
        "plt.xlabel('Months Since February 2008', size=12)\n",
        "plt.ylabel('Cumulative Oil Production (Sm3)', size=12)\n",
        "\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}