01_loaddata.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "mem-01-loaddata.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "HKXnFFVVbEeG",
        "daB9blh79e5t"
      ],
      "authorship_tag": "ABX9TyPg2cpZyIfuTDLYYxgUvS3d",
      "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/mem/blob/master/01_loaddata.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HKXnFFVVbEeG",
        "colab_type": "text"
      },
      "source": [
        "## Load Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1QnoFXLxPCVp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XqkpnyuQ8oyp",
        "colab_type": "text"
      },
      "source": [
        "Access Google Drive to get data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LdWiNyoK-X2G",
        "colab_type": "code",
        "outputId": "8d284112-769c-4dc8-efff-8683b3fe5973",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 120
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BlxY_mqnJyoi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import sys\n",
        "sys.path.append('/content/drive/My Drive/Colab Notebooks')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YEN3-qVtLp68",
        "colab_type": "text"
      },
      "source": [
        "46 LAS files from Norne Field."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nZxn2BQcL7L_",
        "colab_type": "code",
        "outputId": "c76323bd-4669-4af5-f4af-e4f8c6878ee9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 786
        }
      },
      "source": [
        "import glob\n",
        "import os\n",
        "\n",
        "file_path = \"/content/drive/My Drive/Colab Notebooks/well_logs\"\n",
        "read_files = glob.glob(os.path.join(file_path, \"*.las\"))\n",
        "read_files"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['/content/drive/My Drive/Colab Notebooks/well_logs/660810-B-1BH.las',\n",
              " '/content/drive/My Drive/Colab Notebooks/well_logs/660810-E-2AH.las',\n",
              " '/content/drive/My Drive/Colab Notebooks/well_logs/660810-B-1AHT2.las',\n",
              " '/content/drive/My Drive/Colab Notebooks/well_logs/660810-K-1HT2.las',\n",
              " '/content/drive/My Drive/Colab Notebooks/well_logs/660810-B-1H.las',\n",
              " '/content/drive/My Drive/Colab Notebooks/well_logs/660810-B-2H.las',\n",
              " '/content/drive/My Drive/Colab Notebooks/well_logs/660810-B-3H.las',\n",
              " '/content/drive/My Drive/Colab Notebooks/well_logs/660810-B-4AH.las',\n",
              " '/content/drive/My Drive/Colab Notebooks/well_logs/660810-B-4BH.las',\n",
              " '/content/drive/My Drive/Colab Notebooks/well_logs/660810-B-4CH.las',\n",
              " '/content/drive/My Drive/Colab Notebooks/well_logs/660810-B-4DHT2.las',\n",
              " '/content/drive/My Drive/Colab Notebooks/well_logs/660810-B-4H.las',\n",
              " '/content/drive/My Drive/Colab Notebooks/well_logs/660810-C-1H.las',\n",
              " '/content/drive/My Drive/Colab Notebooks/well_logs/660810-C-2H.las',\n",
              " '/content/drive/My Drive/Colab Notebooks/well_logs/660810-C-3H.las',\n",
              " '/content/drive/My Drive/Colab Notebooks/well_logs/660810-C-4AH.las',\n",
              " '/content/drive/My Drive/Colab Notebooks/well_logs/660810-C-4H.las',\n",
              " '/content/drive/My Drive/Colab Notebooks/well_logs/660810-D-1AH.las',\n",
              " '/content/drive/My Drive/Colab Notebooks/well_logs/660810-D-1BH.las',\n",
              " '/content/drive/My Drive/Colab Notebooks/well_logs/660810-D-1CH.las',\n",
              " '/content/drive/My Drive/Colab Notebooks/well_logs/660810-D-1H.las',\n",
              " '/content/drive/My Drive/Colab Notebooks/well_logs/660810-D-2HT2.las',\n",
              " '/content/drive/My Drive/Colab Notebooks/well_logs/660810-D-3AH.las',\n",
              " '/content/drive/My Drive/Colab Notebooks/well_logs/660810-D-3BY1HT2.las',\n",
              " '/content/drive/My Drive/Colab Notebooks/well_logs/660810-D-3BY2H.las',\n",
              " '/content/drive/My Drive/Colab Notebooks/well_logs/660810-D-3H.las',\n",
              " '/content/drive/My Drive/Colab Notebooks/well_logs/660810-D-4AH.las',\n",
              " '/content/drive/My Drive/Colab Notebooks/well_logs/660810-D-4AHT2.las',\n",
              " '/content/drive/My Drive/Colab Notebooks/well_logs/660810-D-4H.las',\n",
              " '/content/drive/My Drive/Colab Notebooks/well_logs/660810-E-1H.las',\n",
              " '/content/drive/My Drive/Colab Notebooks/well_logs/660810-E-2H.las',\n",
              " '/content/drive/My Drive/Colab Notebooks/well_logs/660810-E-3AH.las',\n",
              " '/content/drive/My Drive/Colab Notebooks/well_logs/660810-E-3AHT2.las',\n",
              " '/content/drive/My Drive/Colab Notebooks/well_logs/660810-E-3BH.las',\n",
              " '/content/drive/My Drive/Colab Notebooks/well_logs/660810-E-3CHT2.las',\n",
              " '/content/drive/My Drive/Colab Notebooks/well_logs/660810-E-3H.las',\n",
              " '/content/drive/My Drive/Colab Notebooks/well_logs/660810-E-4AH.las',\n",
              " '/content/drive/My Drive/Colab Notebooks/well_logs/660810-E-4AHT2.las',\n",
              " '/content/drive/My Drive/Colab Notebooks/well_logs/660810-E-4H.las',\n",
              " '/content/drive/My Drive/Colab Notebooks/well_logs/660810-E-4HT2.las',\n",
              " '/content/drive/My Drive/Colab Notebooks/well_logs/660810-F-1H.las',\n",
              " '/content/drive/My Drive/Colab Notebooks/well_logs/660810-F-2H.las',\n",
              " '/content/drive/My Drive/Colab Notebooks/well_logs/660810-F-3H.las',\n",
              " '/content/drive/My Drive/Colab Notebooks/well_logs/660810-F-4H.las',\n",
              " '/content/drive/My Drive/Colab Notebooks/well_logs/660810-K-1H.las',\n",
              " '/content/drive/My Drive/Colab Notebooks/well_logs/660810-K-3H.las']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dL4Eop9jS1Wm",
        "colab_type": "text"
      },
      "source": [
        "Get the name of the well from the file."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r_sNq1evP57g",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "well_names = []\n",
        "\n",
        "for files in read_files:\n",
        "  files = os.path.splitext(os.path.basename(files))[0]\n",
        "  well_names.append(files)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GlhtC0vo0psz",
        "colab_type": "code",
        "outputId": "3fe7feb1-cbcf-42fe-ffea-b45953c6ea19",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 786
        }
      },
      "source": [
        "well_names"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['660810-B-1BH',\n",
              " '660810-E-2AH',\n",
              " '660810-B-1AHT2',\n",
              " '660810-K-1HT2',\n",
              " '660810-B-1H',\n",
              " '660810-B-2H',\n",
              " '660810-B-3H',\n",
              " '660810-B-4AH',\n",
              " '660810-B-4BH',\n",
              " '660810-B-4CH',\n",
              " '660810-B-4DHT2',\n",
              " '660810-B-4H',\n",
              " '660810-C-1H',\n",
              " '660810-C-2H',\n",
              " '660810-C-3H',\n",
              " '660810-C-4AH',\n",
              " '660810-C-4H',\n",
              " '660810-D-1AH',\n",
              " '660810-D-1BH',\n",
              " '660810-D-1CH',\n",
              " '660810-D-1H',\n",
              " '660810-D-2HT2',\n",
              " '660810-D-3AH',\n",
              " '660810-D-3BY1HT2',\n",
              " '660810-D-3BY2H',\n",
              " '660810-D-3H',\n",
              " '660810-D-4AH',\n",
              " '660810-D-4AHT2',\n",
              " '660810-D-4H',\n",
              " '660810-E-1H',\n",
              " '660810-E-2H',\n",
              " '660810-E-3AH',\n",
              " '660810-E-3AHT2',\n",
              " '660810-E-3BH',\n",
              " '660810-E-3CHT2',\n",
              " '660810-E-3H',\n",
              " '660810-E-4AH',\n",
              " '660810-E-4AHT2',\n",
              " '660810-E-4H',\n",
              " '660810-E-4HT2',\n",
              " '660810-F-1H',\n",
              " '660810-F-2H',\n",
              " '660810-F-3H',\n",
              " '660810-F-4H',\n",
              " '660810-K-1H',\n",
              " '660810-K-3H']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dsob71wW9DkT",
        "colab_type": "text"
      },
      "source": [
        "If the well name too long, shorten it by renaming it."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n739AJgA1YuL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "well_names = np.array(well_names)\n",
        "wellnames = np.array(['B1BH', 'E2AH', 'B1AHT2', 'K1HT2',\n",
        "                      'B1H', 'B2H', 'B3H', 'B4AH',\n",
        "                      'B4BH', 'B4CH', 'B4DHT2', 'B4H',\n",
        "                      'C1H', 'C2H', 'C3H', 'C4AH',\n",
        "                      'C4H', 'D1AH', 'D1BH', 'D1CH',\n",
        "                      'D1H', 'D2HT2', 'D3AH', 'D3BY1HT2',\n",
        "                      'D3BY2H', 'D3H', 'D4AH', 'D4AHT2',\n",
        "                      'D4H', 'E1H', 'E2H', 'E3AH',\n",
        "                      'E3AHT2', 'E3BH', 'E3CHT2','E3H',\n",
        "                      'E4AH', 'E4AHT2', 'E4H','E4HT2',\n",
        "                      'F1H', 'F2H', 'F3H', \n",
        "                      'F4H','K1H', 'K3H'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m_JgY_dkS3ex",
        "colab_type": "text"
      },
      "source": [
        "Import `lasio` library to import LAS data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UGS5OIAr6DnQ",
        "colab_type": "code",
        "outputId": "ab7ef0c6-adaa-440e-b84f-ae49cc56b3a6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 100
        }
      },
      "source": [
        "!pip install lasio"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting lasio\n",
            "  Downloading https://files.pythonhosted.org/packages/94/fa/b06aa199d8eaf85a8e6d417021d2aeff9b5c59c92c9a669046408b211bc9/lasio-0.24.1-py3-none-any.whl\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from lasio) (1.17.5)\n",
            "Installing collected packages: lasio\n",
            "Successfully installed lasio-0.24.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QpCVaGzA6LFw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import lasio"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7djniJd79P3M",
        "colab_type": "text"
      },
      "source": [
        "Read LAS file (if command: `Header section Parameter regexp=~P was not found.`, it's OK)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q3RJncmx7DZF",
        "colab_type": "code",
        "outputId": "0f07a15b-fbc3-4821-ee21-8d50989318df",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 786
        }
      },
      "source": [
        "lases = []\n",
        "\n",
        "for files in read_files:\n",
        "    las = lasio.read(files)\n",
        "    lases.append(las)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Header section Parameter regexp=~P was not found.\n",
            "Header section Parameter regexp=~P was not found.\n",
            "Header section Parameter regexp=~P was not found.\n",
            "Header section Parameter regexp=~P was not found.\n",
            "Header section Parameter regexp=~P was not found.\n",
            "Header section Parameter regexp=~P was not found.\n",
            "Header section Parameter regexp=~P was not found.\n",
            "Header section Parameter regexp=~P was not found.\n",
            "Header section Parameter regexp=~P was not found.\n",
            "Header section Parameter regexp=~P was not found.\n",
            "Header section Parameter regexp=~P was not found.\n",
            "Header section Parameter regexp=~P was not found.\n",
            "Header section Parameter regexp=~P was not found.\n",
            "Header section Parameter regexp=~P was not found.\n",
            "Header section Parameter regexp=~P was not found.\n",
            "Header section Parameter regexp=~P was not found.\n",
            "Header section Parameter regexp=~P was not found.\n",
            "Header section Parameter regexp=~P was not found.\n",
            "Header section Parameter regexp=~P was not found.\n",
            "Header section Parameter regexp=~P was not found.\n",
            "Header section Parameter regexp=~P was not found.\n",
            "Header section Parameter regexp=~P was not found.\n",
            "Header section Parameter regexp=~P was not found.\n",
            "Header section Parameter regexp=~P was not found.\n",
            "Header section Parameter regexp=~P was not found.\n",
            "Header section Parameter regexp=~P was not found.\n",
            "Header section Parameter regexp=~P was not found.\n",
            "Header section Parameter regexp=~P was not found.\n",
            "Header section Parameter regexp=~P was not found.\n",
            "Header section Parameter regexp=~P was not found.\n",
            "Header section Parameter regexp=~P was not found.\n",
            "Header section Parameter regexp=~P was not found.\n",
            "Header section Parameter regexp=~P was not found.\n",
            "Header section Parameter regexp=~P was not found.\n",
            "Header section Parameter regexp=~P was not found.\n",
            "Header section Parameter regexp=~P was not found.\n",
            "Header section Parameter regexp=~P was not found.\n",
            "Header section Parameter regexp=~P was not found.\n",
            "Header section Parameter regexp=~P was not found.\n",
            "Header section Parameter regexp=~P was not found.\n",
            "Header section Parameter regexp=~P was not found.\n",
            "Header section Parameter regexp=~P was not found.\n",
            "Header section Parameter regexp=~P was not found.\n",
            "Header section Parameter regexp=~P was not found.\n",
            "Header section Parameter regexp=~P was not found.\n",
            "Header section Parameter regexp=~P was not found.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "daB9blh79e5t",
        "colab_type": "text"
      },
      "source": [
        "## Well catalogue\n",
        "Input the name of the well you want to view in the `find` and check what data is present."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TMK7YG7gBiL7",
        "colab_type": "code",
        "outputId": "b6ededaa-5353-45fd-f24d-684164612901",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 117
        }
      },
      "source": [
        "wellnames"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['B1BH', 'E2AH', 'B1AHT2', 'K1HT2', 'B1H', 'B2H', 'B3H', 'B4AH',\n",
              "       'B4BH', 'B4CH', 'B4DHT2', 'B4H', 'C1H', 'C2H', 'C3H', 'C4AH',\n",
              "       'C4H', 'D1AH', 'D1BH', 'D1CH', 'D1H', 'D2HT2', 'D3AH', 'D3BY1HT2',\n",
              "       'D3BY2H', 'D3H', 'D4AH', 'D4AHT2', 'D4H', 'E1H', 'E2H', 'E3AH',\n",
              "       'E3AHT2', 'E3BH', 'E3CHT2', 'E3H', 'E4AH', 'E4AHT2', 'E4H',\n",
              "       'E4HT2', 'F1H', 'F2H', 'F3H', 'F4H', 'K1H', 'K3H'], dtype='<U8')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MMKBeKOp4Rqo",
        "colab_type": "code",
        "outputId": "138eee2c-166e-4c28-8e72-d4d74a16cc19",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        }
      },
      "source": [
        "# type the numbers from 0 to 45 in the wellames[...]\n",
        "find = wellnames[0]\n",
        "# print the name of the searched well\n",
        "print(\"Well name:\", find)\n",
        "id_ = np.int64(np.where(wellnames==find)).item()\n",
        "print(\"Data available:\", lases[id_].keys())\n",
        "# check more details\n",
        "print(\"Detail about data:\")\n",
        "print(lases[id_].curves)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Well name: B1BH\n",
            "Data available: ['DEPTH', 'TVD', 'TVDSS', 'GR', 'KLOGH', 'NPHI', 'PHIF', 'RHOB', 'SW', 'VSH']\n",
            "Detail about data:\n",
            "Mnemonic  Unit         Value    Description                            \n",
            "--------  ----         -----    -----------                            \n",
            "DEPTH     M                     Measured Depth                         \n",
            "TVD       M                     True Vertical Depth (TVD)              \n",
            "TVDSS     M                     True Vertical Depth (TVDSS)            \n",
            "GR        API          UNKNOWN  GR:UNKNOWN:rC::v0 (auto-composite)     \n",
            "KLOGH     mD           UNKNOWN  KLOGH:UNKNOWN:rC::v0 (auto-composite)  \n",
            "NPHI      v/v_decimal  UNKNOWN  NPHI:UNKNOWN:rC::v0 (auto-composite)   \n",
            "PHIF      v/v_decimal  UNKNOWN  PHIF:UNKNOWN:rC::v0 (auto-composite)   \n",
            "RHOB      g/cm3        UNKNOWN  RHOB:UNKNOWN:rC::v0 (auto-composite)   \n",
            "SW        v/v_decimal  UNKNOWN  SW:UNKNOWN:rC::v0 (auto-composite)     \n",
            "VSH       v/v_decimal  UNKNOWN  VSH:UNKNOWN:rC::v0 (auto-composite)    \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MvPgYnQUW9rG",
        "colab_type": "code",
        "outputId": "61cf1671-063d-44b8-dc6b-c437499ca721",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 622
        }
      },
      "source": [
        "# peek or overviewing the well log\n",
        "\n",
        "# which data you want to see\n",
        "data_view = 'RHOB'\n",
        "\n",
        "print(\"Data\", data_view, \"of well:\", find)\n",
        "\n",
        "plt.figure(figsize=(5,10))\n",
        "plt.plot((lases[id_][data_view]), (lases[id_]['DEPTH']))\n",
        "plt.xlabel(data_view); plt.ylabel(\"Depth (m)\")\n",
        "plt.grid(True)\n",
        "plt.gca().invert_yaxis()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Data RHOB of well: B1BH\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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bgchR1ghTeoxSIcjGYFu6tAFAh4+jxeUDY6oXSJZNpB/FBEBi2gc4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\nTkSuQmCC\n",
            "text/plain": [
              "<Figure size 360x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}