05_Handling_Well_Deviation_Survey.ipynb

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      "name": "05-Handling Well Deviation Survey.ipynb",
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      "source": [
        "<table align=\"center\">\n",
        "  <td align=\"center\"><a target=\"_blank\" href=\"https://colab.research.google.com/drive/11scjsE3K5z76nibxbJu3oVx0OV6RMLQN?usp=sharing\">\n",
        "        <img src=\"https://i.ibb.co/3723Hm9/colab.png\"  style=\"padding-bottom:5px;\" />Run in Google Colab</a></td>\n",
        "  <td align=\"center\"><a target=\"_blank\" href=\"https://github.com/leocd91/geodatahandling\">\n",
        "        <img src=\"https://i.ibb.co/L5p10GH/github.png\"  height=\"70px\" style=\"padding-bottom:5px;\"  />View Source on GitHub</a></td>\n",
        "</table>\n"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pij2PSOOXJ1x",
        "colab_type": "text"
      },
      "source": [
        "# **Introduction** (you can read it or skip it)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ITe_TZChEczk",
        "colab_type": "text"
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      "source": [
        "By : leocd91@gmail.com\n",
        "\n",
        "---\n",
        "Welcome to the fourth episode of \"***Digital Geoscience Data Handling using Python***\" Series.\n",
        "During this physical-distancing-new-normal year, I try to compile my experiences in handling digital geoscience / petrotechnical data. \n",
        "\n",
        "This will include: Parsing any type of data, QC analysis, database, feeding data for geophysical inversion - machine learning / deep learning stuffs, and maybe handling those data to do some GP-GPU too.\n",
        "\n",
        "I made this tutorial as beginner-friendly as possible, so some explanation might not be as \"*cool & marketing-friendly*\" as developers intended. \n",
        "And some code are not *pythonic* for that reason too.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "any feedback just hmu.\n",
        "\n",
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jwg3LODIctIj",
        "colab_type": "text"
      },
      "source": [
        "**- Why don't we just install X to read those files?**\n",
        "\n",
        "> Yes you can do that too. \n",
        "\n",
        "> This tutorial for those who wish how to parse any digital geoscience data content without dependencies. \n",
        "> People who work with geoscience data for some years often struggles with python package that evolve really fast which make their code obsolete quickly. Also some package use their own class/data-type which you will find challenging to inspect your own array."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2ZguU1ugJUAd",
        "colab_type": "text"
      },
      "source": [
        "**- Is there any Prereq.?**\n",
        "\n",
        "> Just knowing basic stuff about programming is enough.\n",
        "If you don't even know what an array is, I suggest watching some crash course python on youtube."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IPdihNIKLSie",
        "colab_type": "text"
      },
      "source": [
        " **- Who are you giving some tutorials on the internet!?**\n",
        "\n",
        " \n",
        "\n",
        "> I'm a Petrotechnical Data Management on a NOC in Indonesia. More than 5 years here and still going strong.\n",
        "\n",
        "> I code my way to finish my undergraduate thesis (FDTD Elastic Wave in cuda) and get some side-gigs from there too (Inversion Method, Numerical Simulation, Etc.).\n",
        "\n",
        "> I know some C, F95, and Matlab (Now I'm Python *Muallaf* tho).\n",
        "\n",
        "> My main interest is in computational geophysics and GP-GPU.\n",
        "\n",
        "> I also *(lazily)* wrote some stuff on my blog about computational geophysics here http://redigitize.blogspot.com/ \n",
        "\n",
        "> I also ***love*** deep-fried banana.\n",
        " \n",
        "\n",
        " \n",
        " **TL;DR** I'm not a PhD, CEO, or Someone famous. I'm just glad that I can share something that I learn and learn more from this."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MKxxEXbmRZIb",
        "colab_type": "text"
      },
      "source": [
        "**- Which Python or Software or IDE or whatever that means to learn python? What is this stuff called Notebook!? Even this google colab thing!?   I hate you!**\n",
        "\n",
        "\n",
        "> Grab your towel and don't panic! \n",
        "> You can use this stuff inside google colab thing which people usually call by \"notebook format\", it's easy to make a step-by-step tutorial here. Also to run your specific line of code you just have to click the \"play\" button (it's actually RUN button tho) on the left.\n",
        "\n",
        "> Try this one below!\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7jhWkegLUSjJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "1f2b851c-aac7-4ea4-eb7c-aa8c233df2c3"
      },
      "source": [
        "print(\"hello stranger, what are you buyin'?\") #click run button on the left and see result below."
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "hello stranger, what are you buyin'?\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2FToGcsCUrPR",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "> As for what \"*software*\" to use, For me, I rarely use a notebook, some people that prefer IDE or Editor type of python use pycharm (https://www.jetbrains.com/pycharm/) or SPYDER (https://www.spyder-ide.org/) (for former Matlab user, maybe spyder is your favorite editor. It got variable explorer!)\n",
        "\n",
        "**TL;DR** you can use google colab notebook or install pycharm or install anaconda (got spyder and stuff installed in your pc already like google colab)\n",
        "\n",
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SqnzgER3GQdC",
        "colab_type": "text"
      },
      "source": [
        "# **Deviation Trajectory Data Handling**\n",
        "---\n",
        "Now how do we handle digital trajectory / deviation survey data using Python?\n",
        "\n",
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GdeORNGhmGpy",
        "colab_type": "text"
      },
      "source": [
        "First, just like the last tutorial, We put this 3 lines of code on top of our code. \n",
        "\n",
        "<del>We have this habit to always use this just like some cult chants to summon a Dark Lord.</del>\n",
        "```\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "```\n",
        "\n",
        "we need `numpy` to make array and some scientific function to calculate the deviation survey method, \n",
        "\n",
        "`pandas` for... well.. uh.. the dataframe , \n",
        "and `matplotlib` to plot those data.\n",
        "\n",
        "---"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c7ie_mgV_ztR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np \n",
        "import pandas as pd \n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SEUzZzqu1wMi",
        "colab_type": "text"
      },
      "source": [
        "Set up the pandas table look on colab to make it more readable."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-VukOel7BdQl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "e0966926-b958-4767-c9da-11a3d3d5f610"
      },
      "source": [
        "%load_ext google.colab.data_table \n",
        "#setting display table (only for google colab)"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The google.colab.data_table extension is already loaded. To reload it, use:\n",
            "  %reload_ext google.colab.data_table\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2VwBzP3_15MH",
        "colab_type": "text"
      },
      "source": [
        "---\n",
        "There are a lot of format for well trajectory / deviation survey. Commonly in ASCII with `.prn` or `.asc` extension and in spreadsheet format.\n",
        "\n",
        "For this tutorial let's use this sample in ASCII format."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ysZU60I3SnTA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 223
        },
        "outputId": "8eefa9ef-c6ef-4e0c-bd79-a1277b915b21"
      },
      "source": [
        "!wget \"https://raw.githubusercontent.com/leocd91/geodatahandling/master/sample_data/well-001_dev_survey.prn\""
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-06-24 06:54:16--  https://raw.githubusercontent.com/leocd91/geodatahandling/master/sample_data/well-001_dev_survey.prn\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 1486 (1.5K) [text/plain]\n",
            "Saving to: ‘well-001_dev_survey.prn.1’\n",
            "\n",
            "\r          well-001_   0%[                    ]       0  --.-KB/s               \rwell-001_dev_survey 100%[===================>]   1.45K  --.-KB/s    in 0s      \n",
            "\n",
            "2020-06-24 06:54:16 (25.6 MB/s) - ‘well-001_dev_survey.prn.1’ saved [1486/1486]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6GuvwyCE2iXq",
        "colab_type": "text"
      },
      "source": [
        "Let's read the data into pandas dataframe."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fbbqtHvfAZf6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_raw = pd.read_csv('well-001_dev_survey.prn', delimiter=',')"
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TV4Z5Z5EA7OR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 635
        },
        "outputId": "dad2bf31-2dfe-4141-de1a-af4e670eab6f"
      },
      "source": [
        "df_raw"
      ],
      "execution_count": 43,
      "outputs": [
        {
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          'v': 45,\n            'f': \"45\",\n        },\n{\n            'v': 1310.5,\n            'f': \"1310.5\",\n        },\n{\n            'v': 42.0,\n            'f': \"42.0\",\n        },\n\"N 50.87 E\"],\n [{\n            'v': 46,\n            'f': \"46\",\n        },\n{\n            'v': 1315.1,\n            'f': \"1315.1\",\n        },\n{\n            'v': 42.8,\n            'f': \"42.8\",\n        },\n\"N 50.35 E\"],\n [{\n            'v': 47,\n            'f': \"47\",\n        },\n{\n            'v': 1341.3,\n            'f': \"1341.3\",\n        },\n{\n            'v': 41.6,\n            'f': \"41.6\",\n        },\n\"N 55.26 E\"],\n [{\n            'v': 48,\n            'f': \"48\",\n        },\n{\n            'v': 1353.8,\n            'f': \"1353.8\",\n        },\n{\n            'v': 41.0,\n            'f': \"41.0\",\n        },\n\"N 55.77 E\"],\n [{\n            'v': 49,\n            'f': \"49\",\n        },\n{\n            'v': 1362.6,\n            'f': \"1362.6\",\n        },\n{\n            'v': 41.6,\n            'f': \"41.6\",\n        },\n\"N 55.26 E\"],\n [{\n            'v': 50,\n            'f': \"50\",\n        },\n{\n            'v': 1370.5,\n            'f': \"1370.5\",\n        },\n{\n            'v': 41.1,\n            'f': \"41.1\",\n        },\n\"N 55.99 E\"],\n [{\n            'v': 51,\n            'f': \"51\",\n        },\n{\n            'v': 1380.1,\n            'f': \"1380.1\",\n        },\n{\n            'v': 40.4,\n            'f': \"40.4\",\n        },\n\"N 60.97 E\"],\n [{\n            'v': 52,\n            'f': \"52\",\n        },\n{\n            'v': 1384.4,\n            'f': \"1384.4\",\n        },\n{\n            'v': 40.4,\n            'f': \"40.4\",\n        },\n\"N 60.39 E\"],\n [{\n            'v': 53,\n            'f': \"53\",\n        },\n{\n            'v': 1400.5,\n            'f': \"1400.5\",\n        },\n{\n            'v': 40.9,\n            'f': \"40.9\",\n        },\n\"N 60.9 E\"],\n [{\n            'v': 54,\n            'f': \"54\",\n        },\n{\n            'v': 1396.2,\n            'f': \"1396.2\",\n        },\n{\n            'v': 40.2,\n            'f': \"40.2\",\n        },\n\"N 60.3 E\"],\n [{\n            'v': 55,\n            'f': \"55\",\n        },\n{\n            'v': 1406.4,\n            'f': \"1406.4\",\n        },\n{\n            'v': 40.8,\n            'f': \"40.8\",\n        },\n\"N 60.13 E\"],\n [{\n            'v': 56,\n            'f': \"56\",\n        },\n{\n            'v': 1418.5,\n            'f': \"1418.5\",\n        },\n{\n            'v': 40.3,\n            'f': \"40.3\",\n        },\n\"N 60.2 E\"],\n [{\n            'v': 57,\n            'f': \"57\",\n        },\n{\n            'v': 1420.6,\n            'f': \"1420.6\",\n        },\n{\n            'v': 40.5,\n            'f': \"40.5\",\n        },\n\"N 60.26 E\"],\n [{\n            'v': 58,\n            'f': \"58\",\n        },\n{\n            'v': 1429.7,\n            'f': \"1429.7\",\n        },\n{\n            'v': 40.6,\n            'f': \"40.6\",\n        },\n\"N 60.16 E\"],\n [{\n            'v': 59,\n            'f': \"59\",\n        },\n{\n            'v': 1435.2,\n            'f': \"1435.2\",\n        },\n{\n            'v': 40.0,\n            'f': \"40.0\",\n        },\n\"N 60.61 E\"],\n [{\n            'v': 60,\n            'f': \"60\",\n        },\n{\n            'v': 1442.6,\n            'f': \"1442.6\",\n        },\n{\n            'v': 40.1,\n            'f': \"40.1\",\n        },\n\"N 63.31 E\"],\n [{\n            'v': 61,\n            'f': \"61\",\n        },\n{\n            'v': 1447.1,\n            'f': \"1447.1\",\n        },\n{\n            'v': 40.2,\n            'f': \"40.2\",\n        },\n\"N 63.06 E\"],\n [{\n            'v': 62,\n            'f': \"62\",\n        },\n{\n            'v': 1450.6,\n            'f': \"1450.6\",\n        },\n{\n            'v': 40.3,\n            'f': \"40.3\",\n        },\n\"N 63.37 E\"],\n [{\n            'v': 63,\n            'f': \"63\",\n        },\n{\n            'v': 1463.5,\n            'f': \"1463.5\",\n        },\n{\n            'v': 40.2,\n            'f': \"40.2\",\n        },\n\"N 63.54 E\"],\n [{\n            'v': 64,\n            'f': \"64\",\n        },\n{\n            'v': 1481.4,\n            'f': \"1481.4\",\n        },\n{\n            'v': 40.8,\n            'f': \"40.8\",\n        },\n\"N 63.12 E\"],\n [{\n            'v': 65,\n            'f': \"65\",\n        },\n{\n            'v': 1479.8,\n            'f': \"1479.8\",\n        },\n{\n            'v': 40.4,\n            'f': \"40.4\",\n        },\n\"N 63.46 E\"],\n [{\n            'v': 66,\n            'f': \"66\",\n        },\n{\n            'v': 1483.8,\n            'f': \"1483.8\",\n        },\n{\n            'v': 40.4,\n            'f': \"40.4\",\n        },\n\"N 63.45 E\"],\n [{\n            'v': 67,\n            'f': \"67\",\n        },\n{\n            'v': 1493.0,\n            'f': \"1493.0\",\n        },\n{\n            'v': 41.1,\n            'f': \"41.1\",\n        },\n\"N 63.6 E\"],\n [{\n            'v': 68,\n            'f': \"68\",\n        },\n{\n            'v': 1532.5,\n            'f': \"1532.5\",\n        },\n{\n            'v': 41.5,\n            'f': \"41.5\",\n        },\n\"N 64.38 E\"],\n [{\n            'v': 69,\n            'f': \"69\",\n        },\n{\n            'v': 1540.5,\n            'f': \"1540.5\",\n        },\n{\n            'v': 41.0,\n            'f': \"41.0\",\n        },\n\"N 64.81 E\"]],\n        columns: [[\"number\", \"index\"], [\"number\", \"MD(M)\"], [\"number\", \"Inclination\"], [\"string\", \"Azimuth\"]],\n        columnOptions: [{\"width\": \"1px\", \"className\": \"index_column\"}],\n        rowsPerPage: 25,\n        helpUrl: \"https://colab.research.google.com/notebooks/data_table.ipynb\",\n        suppressOutputScrolling: true,\n        minimumWidth: undefined,\n      });\n    ",
            "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>MD(M)</th>\n",
              "      <th>Inclination</th>\n",
              "      <th>Azimuth</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>N 0 E</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>168.9</td>\n",
              "      <td>0.0</td>\n",
              "      <td>N 0 E</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>178.5</td>\n",
              "      <td>2.5</td>\n",
              "      <td>N 1 E</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>190.4</td>\n",
              "      <td>4.5</td>\n",
              "      <td>N 1.3 E</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>210.9</td>\n",
              "      <td>5.9</td>\n",
              "      <td>N 1.6 E</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65</th>\n",
              "      <td>1479.8</td>\n",
              "      <td>40.4</td>\n",
              "      <td>N 63.46 E</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66</th>\n",
              "      <td>1483.8</td>\n",
              "      <td>40.4</td>\n",
              "      <td>N 63.45 E</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67</th>\n",
              "      <td>1493.0</td>\n",
              "      <td>41.1</td>\n",
              "      <td>N 63.6 E</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>68</th>\n",
              "      <td>1532.5</td>\n",
              "      <td>41.5</td>\n",
              "      <td>N 64.38 E</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>69</th>\n",
              "      <td>1540.5</td>\n",
              "      <td>41.0</td>\n",
              "      <td>N 64.81 E</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>70 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     MD(M)  Inclination    Azimuth\n",
              "0      0.0          0.0      N 0 E\n",
              "1    168.9          0.0      N 0 E\n",
              "2    178.5          2.5      N 1 E\n",
              "3    190.4          4.5    N 1.3 E\n",
              "4    210.9          5.9    N 1.6 E\n",
              "..     ...          ...        ...\n",
              "65  1479.8         40.4  N 63.46 E\n",
              "66  1483.8         40.4  N 63.45 E\n",
              "67  1493.0         41.1   N 63.6 E\n",
              "68  1532.5         41.5  N 64.38 E\n",
              "69  1540.5         41.0  N 64.81 E\n",
              "\n",
              "[70 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FeY4u8Ib3PID",
        "colab_type": "text"
      },
      "source": [
        "---\n",
        "Nice. \n",
        "\n",
        "But those azimuth it's still in a `string` and bearing format, glad all of them are in **N ... E** so let's just get the number using regex and convert it into `float` (just like the LAS tutorial)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zl7dzbi2B5vK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_raw['Azimuth']=df_raw['Azimuth'].str.extract('(\\d*\\.\\d+|\\d+)')"
      ],
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LtcWXkczDdAb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 92
        },
        "outputId": "52d3e20b-addb-4f93-c3c4-84077072ed1b"
      },
      "source": [
        "df_raw=df_raw.astype('float')\n",
        "df_raw.dtypes"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MD(M)          float64\n",
              "Inclination    float64\n",
              "Azimuth        float64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_IUJqAFT3vuO",
        "colab_type": "text"
      },
      "source": [
        "Let's see the result."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TRGmYnJriTC-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 635
        },
        "outputId": "31e1161e-ec5b-4d83-820c-b90cb0fde5e3"
      },
      "source": [
        "df_raw"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.module+javascript": "\n      import \"https://ssl.gstatic.com/colaboratory/data_table/a6224c040fa35dcf/data_table.js\";\n\n      window.createDataTable({\n        data: [[{\n            'v': 0,\n            'f': \"0\",\n        },\n{\n            'v': 0,\n            'f': \"0\",\n        },\n{\n            'v': 0,\n            'f': \"0\",\n        },\n{\n            'v': 0,\n            'f': \"0\",\n        }],\n [{\n            'v': 1,\n            'f': \"1\",\n        },\n{\n            'v': 168.9,\n            'f': \"168.9\",\n        },\n{\n            'v': 0,\n            'f': \"0\",\n        },\n{\n            'v': 0,\n            'f': \"0\",\n        }],\n [{\n            'v': 2,\n            'f': \"2\",\n        },\n{\n            'v': 178.5,\n            'f': \"178.5\",\n        },\n{\n            'v': 2.5,\n            'f': \"2.5\",\n        },\n{\n            'v': 1,\n            'f': \"1\",\n        }],\n [{\n            'v': 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          'f': \"41\",\n        },\n{\n            'v': 64.81,\n            'f': \"64.81\",\n        }]],\n        columns: [[\"number\", \"index\"], [\"number\", \"MD(M)\"], [\"number\", \"Inclination\"], [\"number\", \"Azimuth\"]],\n        columnOptions: [{\"width\": \"1px\", \"className\": \"index_column\"}],\n        rowsPerPage: 25,\n        helpUrl: \"https://colab.research.google.com/notebooks/data_table.ipynb\",\n        suppressOutputScrolling: true,\n        minimumWidth: undefined,\n      });\n    ",
            "text/html": [
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              "        text-align: right;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>MD(M)</th>\n",
              "      <th>Inclination</th>\n",
              "      <th>Azimuth</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>168.9</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>178.5</td>\n",
              "      <td>2.5</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>190.4</td>\n",
              "      <td>4.5</td>\n",
              "      <td>1.30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>210.9</td>\n",
              "      <td>5.9</td>\n",
              "      <td>1.60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65</th>\n",
              "      <td>1479.8</td>\n",
              "      <td>40.4</td>\n",
              "      <td>63.46</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66</th>\n",
              "      <td>1483.8</td>\n",
              "      <td>40.4</td>\n",
              "      <td>63.45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67</th>\n",
              "      <td>1493.0</td>\n",
              "      <td>41.1</td>\n",
              "      <td>63.60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>68</th>\n",
              "      <td>1532.5</td>\n",
              "      <td>41.5</td>\n",
              "      <td>64.38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>69</th>\n",
              "      <td>1540.5</td>\n",
              "      <td>41.0</td>\n",
              "      <td>64.81</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>70 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     MD(M)  Inclination  Azimuth\n",
              "0      0.0          0.0     0.00\n",
              "1    168.9          0.0     0.00\n",
              "2    178.5          2.5     1.00\n",
              "3    190.4          4.5     1.30\n",
              "4    210.9          5.9     1.60\n",
              "..     ...          ...      ...\n",
              "65  1479.8         40.4    63.46\n",
              "66  1483.8         40.4    63.45\n",
              "67  1493.0         41.1    63.60\n",
              "68  1532.5         41.5    64.38\n",
              "69  1540.5         41.0    64.81\n",
              "\n",
              "[70 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kqIJ301b34gu",
        "colab_type": "text"
      },
      "source": [
        "# **Let's Calculate the TVD and stuff**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Op5HiWLAPFrr",
        "colab_type": "text"
      },
      "source": [
        "---\n",
        "There are a lot of method to calculate the **TVD** (True Vertical Depth) and **subsurface coordinate** of well trajectory and this paper from [Farah Omar Farah](https://orkustofnun.is/gogn/unu-gtp-report/UNU-GTP-2013-27.pdf) really informative.\n",
        "\n",
        "Let's use minimum curvature method.\n",
        "\n",
        "Here's the equation :\n",
        "\n",
        "\n",
        " <img src=\"https://s7.gifyu.com/images/MINCUR.png\" width=\"500\"> \n",
        "\n",
        "Now let's make some room first for our calculation.\n",
        "\n",
        " ---\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jZ0mXGwrfPCj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "datalength = df_raw['Azimuth'].shape[0]"
      ],
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j7Cbdt9bZdhY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_com=pd.DataFrame(data=np.zeros([datalength,10],dtype=float),columns=['brad','rf','north','east','dtvd','dr','yoff','xoff','r','tvd'])"
      ],
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9M_DMXBJfir9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 635
        },
        "outputId": "c5412ff9-e552-44ba-c5ec-cb91a0358f33"
      },
      "source": [
        "df_com"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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\"https://colab.research.google.com/notebooks/data_table.ipynb\",\n        suppressOutputScrolling: true,\n        minimumWidth: undefined,\n      });\n    ",
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              "  <thead>\n",
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              "      <th>brad</th>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>65</th>\n",
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              "    <tr>\n",
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              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>68</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>69</th>\n",
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              "      <td>0.0</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>70 rows × 10 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "    brad   rf  north  east  dtvd   dr  yoff  xoff    r  tvd\n",
              "0    0.0  0.0    0.0   0.0   0.0  0.0   0.0   0.0  0.0  0.0\n",
              "1    0.0  0.0    0.0   0.0   0.0  0.0   0.0   0.0  0.0  0.0\n",
              "2    0.0  0.0    0.0   0.0   0.0  0.0   0.0   0.0  0.0  0.0\n",
              "3    0.0  0.0    0.0   0.0   0.0  0.0   0.0   0.0  0.0  0.0\n",
              "4    0.0  0.0    0.0   0.0   0.0  0.0   0.0   0.0  0.0  0.0\n",
              "..   ...  ...    ...   ...   ...  ...   ...   ...  ...  ...\n",
              "65   0.0  0.0    0.0   0.0   0.0  0.0   0.0   0.0  0.0  0.0\n",
              "66   0.0  0.0    0.0   0.0   0.0  0.0   0.0   0.0  0.0  0.0\n",
              "67   0.0  0.0    0.0   0.0   0.0  0.0   0.0   0.0  0.0  0.0\n",
              "68   0.0  0.0    0.0   0.0   0.0  0.0   0.0   0.0  0.0  0.0\n",
              "69   0.0  0.0    0.0   0.0   0.0  0.0   0.0   0.0  0.0  0.0\n",
              "\n",
              "[70 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1FkOvePR5j4h",
        "colab_type": "text"
      },
      "source": [
        "---\n",
        "Now after put the equation and calculate all of them to the dataframe."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hI-bMIh7Zj57",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for i in range(1,datalength):\n",
        "  df_com['brad'].values[i]=np.arccos(np.cos((df_raw['Inclination'].values[i]-df_raw['Inclination'].values[i-1])*(np.pi/180.0))-\n",
        "                                   (np.sin(df_raw['Inclination'].values[i-1]*(np.pi/180.0))*np.sin(df_raw['Inclination'].values[i]*(np.pi/180.0)))*\n",
        "                                    (1.0-np.cos((df_raw['Azimuth'].values[i]-df_raw['Azimuth'].values[i-1])*(np.pi/180.0))))\n",
        "  if df_com['brad'].values[i]==0.0:\n",
        "    df_com['rf'].values[i]=0.0\n",
        "  else:\n",
        "    df_com['rf'].values[i]=(2.0/df_com['brad'].values[i])*np.tan(df_com['brad'].values[i]/2.0)\n",
        "\n",
        "  if df_com['brad'].values[i]==0.0:\n",
        "    df_com['north'].values[i]=(df_raw['MD(M)'].values[i]-df_raw['MD(M)'].values[i-1])*np.sin(df_raw['Inclination'].values[i]*(np.pi/180.0))*np.cos(df_raw['Azimuth'].values[i]*(np.pi/180.0))\n",
        "  else:\n",
        "    df_com['north'].values[i]=(df_raw['MD(M)'].values[i]-df_raw['MD(M)'].values[i-1])/2.0*((np.sin(df_raw['Inclination'].values[i-1]*(np.pi/180.0))*np.cos(df_raw['Azimuth'].values[i-1]*(np.pi/180.0)))+\n",
        "                                                                                          (np.sin(df_raw['Inclination'].values[i]*(np.pi/180.0))*np.cos(df_raw['Azimuth'].values[i]*(np.pi/180.0))))*df_com['rf'].values[i]\n",
        "\n",
        "  if df_com['brad'].values[i]==0.0:\n",
        "    df_com['east'].values[i]=(df_raw['MD(M)'].values[i]-df_raw['MD(M)'].values[i-1])*np.sin(df_raw['Inclination'].values[i]*(np.pi/180.0))*np.sin(df_raw['Azimuth'].values[i]*(np.pi/180.0))\n",
        "  else:\n",
        "    df_com['east'].values[i]=(df_raw['MD(M)'].values[i]-df_raw['MD(M)'].values[i-1])/2.0*((np.sin(df_raw['Inclination'].values[i-1]*(np.pi/180.0))*np.sin(df_raw['Azimuth'].values[i-1]*(np.pi/180.0)))+\n",
        "                                                                                          (np.sin(df_raw['Inclination'].values[i]*(np.pi/180.0))*np.sin(df_raw['Azimuth'].values[i]*(np.pi/180.0))))*df_com['rf'].values[i]\n",
        "\n",
        "  df_com['dr'].values[i]=np.sqrt((df_com['north'].values[i]**2)+(df_com['east'].values[i]**2))\n",
        "  df_com['r'].values[i]=df_com['r'].values[i-1]+df_com['dr'].values[i]\n",
        "  df_com['xoff'].values[i]=df_com['xoff'].values[i-1]+df_com['east'].values[i]\n",
        "  df_com['yoff'].values[i]=df_com['yoff'].values[i-1]+df_com['north'].values[i]\n",
        "\n",
        "  if df_com['brad'].values[i]==0.0:\n",
        "    df_com['dtvd'].values[i]=(df_raw['MD(M)'].values[i]-df_raw['MD(M)'].values[i-1])*np.cos(df_raw['Inclination'].values[i]*(np.pi/180.0))\n",
        "  else:\n",
        "    df_com['dtvd'].values[i]=(df_raw['MD(M)'].values[i]-df_raw['MD(M)'].values[i-1])/2.0*((np.cos(df_raw['Inclination'].values[i-1]*(np.pi/180.0))+\n",
        "                                                                                          np.cos(df_raw['Inclination'].values[i]*(np.pi/180.0)))*df_com['rf'].values[i])\n",
        "  df_com['tvd'].values[i]=df_com['tvd'].values[i-1]+df_com['dtvd'].values[i]"
      ],
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wvpkFv4BGTrG",
        "colab_type": "text"
      },
      "source": [
        "... and boom! here's our computed result using Minimum Curvature.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pu1Dc37bGc7d",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 650
        },
        "outputId": "fa1436e1-3c2f-445c-d5c5-4870f45a8a55"
      },
      "source": [
        "df_com"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "1   0.000000  0.000000   0.000000  ...    0.000000    0.000000   168.900000\n",
              "2   0.043633  1.000159   0.209374  ...    0.003655    0.209406   178.496954\n",
              "3   0.034908  1.000102   0.726281  ...    0.018777    0.935845   190.374155\n",
              "4   0.024439  1.000050   1.857304  ...    0.066443    2.793760   210.789278\n",
              "..       ...       ...        ...  ...         ...         ...          ...\n",
              "65  0.007978  1.000005  -0.468020  ...  424.370762  716.287352  1240.637910\n",
              "66  0.000113  1.000000   1.158581  ...  426.689952  718.879831  1243.684064\n",
              "67  0.012336  1.000013   2.677178  ...  432.065538  724.885180  1250.653620\n",
              "68  0.011378  1.000011  11.431609  ...  455.495040  750.954764  1280.328693\n",
              "69  0.010032  1.000008   2.263019  ...  460.259644  756.229488  1286.343404\n",
              "\n",
              "[70 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2xRp0d_-C3yV",
        "colab_type": "text"
      },
      "source": [
        "We calculated the TVD. \n",
        "\n",
        "Now let's put it on our main dataframe."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PaT3XdFGx_3H",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_raw['tvd']=df_com['tvd']"
      ],
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nON7vj-KC_-g",
        "colab_type": "text"
      },
      "source": [
        "---\n",
        "Now some visualization!\n",
        "\n",
        "Let's plot it into a polar map.\n",
        "\n",
        "---"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ckZYpyD1D_vN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 308
        },
        "outputId": "2cacdc9a-cc89-42b7-f571-17024dccc06f"
      },
      "source": [
        "ax = plt.subplot(111, polar=True)\n",
        "ax.plot( df_raw['Azimuth'].astype('float').values*np.pi/180.0, df_raw['tvd'].values,'or', alpha=0.5, linewidth=3)\n",
        "ax.set_theta_direction(-1)\n",
        "ax.grid(True)\n",
        "ax.set_theta_zero_location(\"N\")\n",
        "ax.set_title(\"Drilling Direction\",pad=20)\n",
        "pos=ax.get_rlabel_position()\n",
        "ax.set_rlabel_position(pos+110)\n",
        "plt.show()"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-IXAjFgqDPBr",
        "colab_type": "text"
      },
      "source": [
        "...and some well path position. Let's put the r into our main dataframe first."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T2y1Acw9Gq2u",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_raw['r']=df_com['r']"
      ],
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GV5YZen_RBAu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        },
        "outputId": "7f4eac1b-0dc2-4bed-accf-eb3075aa337b"
      },
      "source": [
        "ax2 = plt.subplot(111, aspect=2)\n",
        "ax2.plot( df_raw['r'].values, df_raw['tvd'].values, linewidth=3)\n",
        "ax2.grid(True)\n",
        "ax2.set_title(\"Drilling Deviation\",pad=20)\n",
        "ax2.set_ylabel('TVD(m)')\n",
        "ax2.set_xlabel('Horizontal Displacement(m)')\n",
        "ax2.set_ylim([0, df_raw['tvd'].max()])\n",
        "ax2.invert_yaxis()\n",
        "plt.show()"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7AshCWegaMVI",
        "colab_type": "text"
      },
      "source": [
        "---\n",
        "We see that we can handle, calculate, and visualize the well deviation survey too.\n",
        "\n",
        "Alright, that's it!\n",
        "I hope you can learn something from this tutorial.\n",
        "\n",
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XoWzrsN9M5ep",
        "colab_type": "text"
      },
      "source": [
        "# **EXERCISES**\n",
        "\n",
        "1. Can you calculate the deviation survey using different method? \n",
        "  - Average angle\n",
        "  - Tangential\n",
        "  - Radius of curvature\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3_NRVxaDM_oh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "(code here)"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}