09 - Visualising Data Coverage - Multi Well.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualising Well Data Coverage Using Matplotlib\n",
    "**Created by:** Andy McDonald\n",
    "\n",
    "This notebook illustrates how you can visualise data extent for multiple wells.  \n",
    "**Data Used:** Volve Dataset  \n",
    "**Medium Article:** https://andymcdonaldgeo.medium.com/visualising-well-data-coverage-using-matplotlib-f30591c89754"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exploratory Data Analysis (EDA) is an integral part of Data Science. The same is true for the petrophysical domain and can often be referred to as the Log QC or data review stage of a project. It is at this stage that we begin to go through the data in detail and identify what data we really have, where we have it and what is the quality of the gathered data.  \n",
    "  \n",
    "A significant portion of the time that we spend (in some cases up to 90%! - Kohlleffel, 2015) working with well log data is spent trying to understand it and wrangle it into a state that is fit for interpretation. The remaining 10% is when we can get down to the business of carrying out the petrophysical interpretation. This can vary depending on the initial state of the project being worked on.  \n",
    "  \n",
    "At the QC stage, we often we find ourselves with multiple input files, random curve names, missing data and extra curves that have no immediate use. This can lead to confusion and frustration, especially when working with multiple tools and vintage datasets. In cases where we have missing data, we need to identify it and determine the best way to handle it. This can be difficult to do by looking at single LAS files in a text editor, but it can be made easier using software. One such method is by using Python, a common and popular programming language."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the Data and Libraries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As with any Python project we need to load in the required data and libraries. For this notebook we will be using pandas and matplotlib."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('Data/VolveWells.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>WELL</th>\n",
       "      <th>DEPTH</th>\n",
       "      <th>GR</th>\n",
       "      <th>AC</th>\n",
       "      <th>ACS</th>\n",
       "      <th>BS</th>\n",
       "      <th>CALI</th>\n",
       "      <th>DEN</th>\n",
       "      <th>NEU</th>\n",
       "      <th>PEF</th>\n",
       "      <th>RDEP</th>\n",
       "      <th>RMED</th>\n",
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       "      <td>146.1</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>3</th>\n",
       "      <td>15/9-F-1 C</td>\n",
       "      <td>146.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>4</th>\n",
       "      <td>15/9-F-1 C</td>\n",
       "      <td>146.3</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         WELL  DEPTH  GR  AC  ACS  BS  CALI  DEN  NEU  PEF  RDEP  RMED\n",
       "0  15/9-F-1 C  145.9 NaN NaN  NaN NaN   NaN  NaN  NaN  NaN   NaN   NaN\n",
       "1  15/9-F-1 C  146.0 NaN NaN  NaN NaN   NaN  NaN  NaN  NaN   NaN   NaN\n",
       "2  15/9-F-1 C  146.1 NaN NaN  NaN NaN   NaN  NaN  NaN  NaN   NaN   NaN\n",
       "3  15/9-F-1 C  146.2 NaN NaN  NaN NaN   NaN  NaN  NaN  NaN   NaN   NaN\n",
       "4  15/9-F-1 C  146.3 NaN NaN  NaN NaN   NaN  NaN  NaN  NaN   NaN   NaN"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From a first glance, we can see that we have 12 columns in our dataset. The first column is the Well, followed by the Depth curve, which is subsequently followed by each of the logging curves.  \n",
    "  \n",
    "We can see what wells we have in our dataset by using a simple for loop. This provides a nicely formatted view."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15/9-F-1 C\n",
      "15/9-F-4\n",
      "15/9-F-7\n"
     ]
    }
   ],
   "source": [
    "for well in data['WELL'].unique():\n",
    "    print(well)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order for our plot to work as intended, we need to modify the column order of our dataset. This can be achieved by first creating a list of the columns in the order that we want."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_cols = ['WELL', 'DEPTH', 'CALI', 'BS', 'GR', 'NEU', 'DEN', 'PEF', 'RDEP', 'RMED', 'AC', 'ACS']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we can replace the existing dataframe with the new column order by passing the list directly into the dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data[plot_cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
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       "      <th>DEPTH</th>\n",
       "      <th>CALI</th>\n",
       "      <th>BS</th>\n",
       "      <th>GR</th>\n",
       "      <th>NEU</th>\n",
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       "      <td>NaN</td>\n",
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       "      <th>4</th>\n",
       "      <td>15/9-F-1 C</td>\n",
       "      <td>146.3</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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "         WELL  DEPTH  CALI  BS  GR  NEU  DEN  PEF  RDEP  RMED  AC  ACS\n",
       "0  15/9-F-1 C  145.9   NaN NaN NaN  NaN  NaN  NaN   NaN   NaN NaN  NaN\n",
       "1  15/9-F-1 C  146.0   NaN NaN NaN  NaN  NaN  NaN   NaN   NaN NaN  NaN\n",
       "2  15/9-F-1 C  146.1   NaN NaN NaN  NaN  NaN  NaN   NaN   NaN NaN  NaN\n",
       "3  15/9-F-1 C  146.2   NaN NaN NaN  NaN  NaN  NaN   NaN   NaN NaN  NaN\n",
       "4  15/9-F-1 C  146.3   NaN NaN NaN  NaN  NaN  NaN   NaN   NaN NaN  NaN"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next step involves creating a copy of our dataframe. This will allow us to keep the original dataframe for further work later in a project."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_nan = data.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In places where we have a real value we will assign it a number which will be dependent on its position in the dataframe. In places where we have a NaN (Not a Number) value, we are going to give it a value of number - 1. This will allow us to shade between one number and another whilst using a single subplot for each well. This keeps things simple and negates the need for creating subplots for each curve in each well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CALI 0\n",
      "BS 1\n",
      "GR 2\n",
      "NEU 3\n",
      "DEN 4\n",
      "PEF 5\n",
      "RDEP 6\n",
      "RMED 7\n",
      "AC 8\n",
      "ACS 9\n"
     ]
    }
   ],
   "source": [
    "for num, col in enumerate(data_nan.columns[2:]):\n",
    "    data_nan[col] = data_nan[col].notnull() * (num + 1)\n",
    "    data_nan[col].replace(0, num, inplace=True)\n",
    "    print(col, num) #Print out the col name and number to verify it works"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DEPTH</th>\n",
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       "      <th>count</th>\n",
       "      <td>66843.000000</td>\n",
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       "      <th>mean</th>\n",
       "      <td>1915.232292</td>\n",
       "      <td>0.225304</td>\n",
       "      <td>1.983573</td>\n",
       "      <td>2.974732</td>\n",
       "      <td>3.225035</td>\n",
       "      <td>4.223479</td>\n",
       "      <td>5.223479</td>\n",
       "      <td>6.722813</td>\n",
       "      <td>7.723157</td>\n",
       "      <td>8.072034</td>\n",
       "      <td>9.063477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1108.591492</td>\n",
       "      <td>0.417786</td>\n",
       "      <td>0.127110</td>\n",
       "      <td>0.156940</td>\n",
       "      <td>0.417608</td>\n",
       "      <td>0.416580</td>\n",
       "      <td>0.416580</td>\n",
       "      <td>0.447613</td>\n",
       "      <td>0.447441</td>\n",
       "      <td>0.258547</td>\n",
       "      <td>0.243821</td>\n",
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       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>145.900000</td>\n",
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       "      <td>3.000000</td>\n",
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       "      <td>7.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>9.000000</td>\n",
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       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>910.546200</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1850.965600</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>9.000000</td>\n",
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       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2859.950000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
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       "      <td>4.000000</td>\n",
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       "      <td>6.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>10.000000</td>\n",
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      "text/plain": [
       "              DEPTH          CALI            BS            GR           NEU  \\\n",
       "count  66843.000000  66843.000000  66843.000000  66843.000000  66843.000000   \n",
       "mean    1915.232292      0.225304      1.983573      2.974732      3.225035   \n",
       "std     1108.591492      0.417786      0.127110      0.156940      0.417608   \n",
       "min      145.900000      0.000000      1.000000      2.000000      3.000000   \n",
       "25%      910.546200      0.000000      2.000000      3.000000      3.000000   \n",
       "50%     1850.965600      0.000000      2.000000      3.000000      3.000000   \n",
       "75%     2859.950000      0.000000      2.000000      3.000000      3.000000   \n",
       "max     4093.900000      1.000000      2.000000      3.000000      4.000000   \n",
       "\n",
       "                DEN           PEF          RDEP          RMED            AC  \\\n",
       "count  66843.000000  66843.000000  66843.000000  66843.000000  66843.000000   \n",
       "mean       4.223479      5.223479      6.722813      7.723157      8.072034   \n",
       "std        0.416580      0.416580      0.447613      0.447441      0.258547   \n",
       "min        4.000000      5.000000      6.000000      7.000000      8.000000   \n",
       "25%        4.000000      5.000000      6.000000      7.000000      8.000000   \n",
       "50%        4.000000      5.000000      7.000000      8.000000      8.000000   \n",
       "75%        4.000000      5.000000      7.000000      8.000000      8.000000   \n",
       "max        5.000000      6.000000      7.000000      8.000000      9.000000   \n",
       "\n",
       "                ACS  \n",
       "count  66843.000000  \n",
       "mean       9.063477  \n",
       "std        0.243821  \n",
       "min        9.000000  \n",
       "25%        9.000000  \n",
       "50%        9.000000  \n",
       "75%        9.000000  \n",
       "max       10.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_nan.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting the Data\n",
    "Now we have come to the plotting stage. In order for each well to plot in a separate subplot, we have to group the dataframe by the well name:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped = data_nan.groupby('WELL')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use ax.fillbetweenx() to fill between our two values for each curve that we set up earlier. For example, CALI has two values to indicate data presence: 1 when there is a real value and 0 when there is a NaN. Similarly, GR has two values: 3 when there is real data and 2 when there is a NaN."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Setup the labels we want to display on the x-axis\n",
    "labels = ['CALI', 'BS', 'GR', 'NEU', 'DEN', 'PEF', 'RDEP', 'RMED', 'AC', 'ACS']\n",
    "\n",
    "#Setup the figure and the subplots\n",
    "fig, axs = plt.subplots(1, 3, figsize=(20,10))\n",
    "\n",
    "#Loop through each well and column in the grouped dataframe\n",
    "for (name, df), ax in zip(grouped, axs.flat):\n",
    "    ax.set_xlim(0,9)\n",
    "    \n",
    "    #Setup the depth range\n",
    "    ax.set_ylim(5000, 0)\n",
    "    \n",
    "    #Create multiple fill betweens for each curve# This is between\n",
    "    # the number representing null values and the number representing\n",
    "    # actual values\n",
    "    \n",
    "    ax.fill_betweenx(df.DEPTH, 0, df.CALI, facecolor='grey')\n",
    "    ax.fill_betweenx(df.DEPTH, 1, df.BS, facecolor='lightgrey')\n",
    "    ax.fill_betweenx(df.DEPTH, 2, df.GR, facecolor='mediumseagreen')\n",
    "    ax.fill_betweenx(df.DEPTH, 3, df.NEU, facecolor='lightblue')\n",
    "    ax.fill_betweenx(df.DEPTH, 4, df.DEN, facecolor='lightcoral')\n",
    "    ax.fill_betweenx(df.DEPTH, 5, df.PEF, facecolor='violet')\n",
    "    ax.fill_betweenx(df.DEPTH, 6, df.RDEP, facecolor='darksalmon')\n",
    "    ax.fill_betweenx(df.DEPTH, 7, df.RMED, facecolor='wheat')\n",
    "    ax.fill_betweenx(df.DEPTH, 8, df.AC, facecolor='thistle')\n",
    "    ax.fill_betweenx(df.DEPTH, 9, df.ACS, facecolor='tan')\n",
    "    \n",
    "    #Setup the grid, axis labels and ticks\n",
    "    ax.grid(axis='x', alpha=0.5, color='black')\n",
    "    ax.set_ylabel('DEPTH (m)', fontsize=14, fontweight='bold')\n",
    "    \n",
    "    #Position vertical lines at the boundaries between the bars\n",
    "    ax.set_xticks([1,2,3,4,5,6,7,8,9,10], minor=False)\n",
    "    \n",
    "    #Position the curve names in the centre of each column\n",
    "    ax.set_xticks([0.5, 1.5 ,2.5 ,3.5 ,4.5 ,5.5 ,6.5 , 7.5, 8.5, 9.5], minor=True)\n",
    "    \n",
    "    #Setup the x-axis tick labels\n",
    "    ax.set_xticklabels(labels,  rotation='vertical', minor=True, verticalalignment='bottom')\n",
    "    ax.set_xticklabels('', minor=False)\n",
    "    ax.tick_params(axis='x', which='minor', pad=-10)\n",
    "    \n",
    "    #Assign the well name as the title to each subplot\n",
    "    ax.set_title(name, fontsize=16, fontweight='bold')\n",
    "\n",
    "plt.savefig('missingdata.png')\n",
    "plt.tight_layout()\n",
    "plt.subplots_adjust(hspace=0.15, wspace=0.25)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From this plot we can determine:  \n",
    "**15/9-F-1 C**\n",
    "* Minor gaps in the gamma ray and resistivity curves. As the gaps appear at same position on both resistivity curves we can make an initial assumption that they may be related to casing shoes. Further investigation would be needed to confirm this.\n",
    "* Nuclear curves (DEN, NEU, PEF) and the caliper are only run over a short section, possibly indicating the zone of interest.\n",
    "* No acoustic curves (AC and ACS)\n",
    "\n",
    "**15/9-F-4**\n",
    "* Contains all available curves, with the majority over a small section towards the bottom of the well. \n",
    "* There are multiple gaps in the gamma ray (GR) and acoustic shear (ACS) curves. Could be tool related. Further investigation would reveal the cause.\n",
    "\n",
    "**15/9-F-7**\n",
    "* Minimal amount of data present over a short and shallow section.\n",
    "* Only bitsize, gamma ray and resistivity measurements presents.\n",
    "* Could potentially be caused by a tool failure or issues encountered whilst drilling. This information could be confirmed by reviewing the End of Well Reports, if they are available."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "display_name": "Python 3.6.4 64-bit ('base': conda)",
   "language": "python",
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   "codemirror_mode": {
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