14 - Displaying Lithology Data.ipynb

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    "# 14 - Displaying Lithology Data Using Python and matplotlib\n",
    "  \n",
    "**Created By:** Andy McDonald  \n",
    "**Link to Article:** https://andymcdonaldgeo.medium.com/displaying-lithology-data-using-python-and-matplotlib-58b4d251ee7a"
   ]
  },
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   "source": [
    "Adding lithology information to a well log plot can enhance a petrophysical or geological interpretation. It can be used to understand why some log responses may behave the way they do. This data may have been sourced from a previous mineralogical interpretation or from mud logs.\n",
    "\n",
    "In my previous article: Enhancing Visualization of Well Logs With Plot Fills, we saw: how to apply fixed color fill between a curve and the edge of a track, how to apply a density-neutron crossover fill and how to apply a variable fill based upon the value of curve being plotted. \n",
    "\n",
    "In this notebook, we will cover how to use a variable fill to apply not only a color to our plot, but also a hatching. Using these two options allows us to generate lithological fill.\n",
    "\n",
    "This notebook forms part of my Python & Petrophysics series. The full series can be found here. For the examples below you can find my Jupyter Notebook and dataset on my GitHub repository at the following link.\n",
    "\n",
    "## Setting up the Libraries \n",
    "The first step is to bring in the libraries we will be working with for this article. We will be using just two libraries: pandas and matplotlib. This will allow us to load our data to a dataframe and display it on a plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the Data\n",
    "The dataset we will be using for this article comes from a recent Machine Learning competition for lithology prediction that was run by Xeek and FORCE (https://xeek.ai/challenges/force-well-logs/overview). The objective of the competition was to predict lithology from a dataset consisting 98 training wells each with varying degrees of log completeness. The objective was to predict lithofacies based on the log measurements. To download the file, navigate to the Data section of the link above.\n",
    "\n",
    "The data can be loaded using pd.read_csv(). As this is quite a large dataset, we will use a single well from it to work with and we will also take just the curves (columns) we need. We can do that by taking a subset of the data like so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "alldata = pd.read_csv('Data/xeek_train_subset.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = alldata.loc[alldata['WELL'] == '16/10-1'][['DEPTH_MD', 'GR', 'RHOB', 'NPHI', 'FORCE_2020_LITHOFACIES_LITHOLOGY']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To make things simpler we will rename the FORCE_2020_LITHOFACIES_LITHOLOGY column to just LITHOLOGY . This is done by using the rename function on the dataframe and passing a dictionary of the old name and the new name. Note that using the `inplace=True` argument will replace the column name in the original dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.rename(columns={'FORCE_2020_LITHOFACIES_LITHOLOGY':'LITHOLOGY'}, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When we call upon our dataframe using `data.head()` we can see that our dataset is small and our lithology column has been renamed."
   ]
  },
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       "        DEPTH_MD         GR  RHOB  NPHI  LITHOLOGY\n",
       "58694  439.41579  51.431892   NaN   NaN      65030\n",
       "58695  439.56779  49.472492   NaN   NaN      65030\n",
       "58696  439.71979  50.167278   NaN   NaN      65030\n",
       "58697  439.87179  46.572552   NaN   NaN      65030\n",
       "58698  440.02379  48.498447   NaN   NaN      65030"
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   "source": [
    "data.head()"
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   "cell_type": "markdown",
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   "source": [
    "## Setting up the Lithologies Using a Nested Dictionary\n",
    "The lithologies currently listed in the lithology column contain a series of numbers. We can map these in a nested dictionary where we have the full name of the lithology, a simplified number (if required for conversion), the hatch style, and the color of the fill.\n",
    "\n",
    "The colors are based on the Kansas Geological Survey website. However, hatching symbols are limited in the default setup of matplotlib. In a future article, I will cover how to create custom hatches and lithologies."
   ]
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       "10"
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     "execution_count": 8,
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   "source": [
    "data['LITHOLOGY'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "lithology_numbers = {30000: {'lith':'Sandstone', 'lith_num':1, 'hatch': '..', 'color':'#ffff00'},\n",
    "                 65030: {'lith':'Sandstone/Shale', 'lith_num':2, 'hatch':'-.', 'color':'#ffe119'},\n",
    "                 65000: {'lith':'Shale', 'lith_num':3, 'hatch':'--', 'color':'#bebebe'},\n",
    "                 80000: {'lith':'Marl', 'lith_num':4, 'hatch':'', 'color':'#7cfc00'},\n",
    "                 74000: {'lith':'Dolomite', 'lith_num':5, 'hatch':'-/', 'color':'#8080ff'},\n",
    "                 70000: {'lith':'Limestone', 'lith_num':6, 'hatch':'+', 'color':'#80ffff'},\n",
    "                 70032: {'lith':'Chalk', 'lith_num':7, 'hatch':'..', 'color':'#80ffff'},\n",
    "                 88000: {'lith':'Halite', 'lith_num':8, 'hatch':'x', 'color':'#7ddfbe'},\n",
    "                 86000: {'lith':'Anhydrite', 'lith_num':9, 'hatch':'', 'color':'#ff80ff'},\n",
    "                 99000: {'lith':'Tuff', 'lith_num':10, 'hatch':'||', 'color':'#ff8c00'},\n",
    "                 90000: {'lith':'Coal', 'lith_num':11, 'hatch':'', 'color':'black'},\n",
    "                 93000: {'lith':'Basement', 'lith_num':12, 'hatch':'-|', 'color':'#ef138a'}}"
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   "cell_type": "code",
   "execution_count": 10,
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     "data": {
      "text/plain": [
       "'#ffff00'"
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     "execution_count": 10,
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   "source": [
    "lithology_numbers[30000]['color']"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "We can quickly convert our nested dictionary to a pandas dataframe to make it easier for humans to read by using the from_dict function like so."
   ]
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   "cell_type": "code",
   "execution_count": 11,
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       "      <th>lith</th>\n",
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       "      <td>Sandstone</td>\n",
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       "      <td>#ffff00</td>\n",
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       "      <td>Sandstone/Shale</td>\n",
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       "      <td>-.</td>\n",
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       "      <td>Limestone</td>\n",
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       "      <td>#80ffff</td>\n",
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       "      <td>Chalk</td>\n",
       "      <td>7</td>\n",
       "      <td>..</td>\n",
       "      <td>#80ffff</td>\n",
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       "      <th>88000</th>\n",
       "      <td>Halite</td>\n",
       "      <td>8</td>\n",
       "      <td>x</td>\n",
       "      <td>#7ddfbe</td>\n",
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       "      <th>86000</th>\n",
       "      <td>Anhydrite</td>\n",
       "      <td>9</td>\n",
       "      <td></td>\n",
       "      <td>#ff80ff</td>\n",
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       "      <th>99000</th>\n",
       "      <td>Tuff</td>\n",
       "      <td>10</td>\n",
       "      <td>||</td>\n",
       "      <td>#ff8c00</td>\n",
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       "      <th>90000</th>\n",
       "      <td>Coal</td>\n",
       "      <td>11</td>\n",
       "      <td></td>\n",
       "      <td>black</td>\n",
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       "      <th>93000</th>\n",
       "      <td>Basement</td>\n",
       "      <td>12</td>\n",
       "      <td>-|</td>\n",
       "      <td>#ef138a</td>\n",
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       "  </tbody>\n",
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      "text/plain": [
       "                      lith  lith_num hatch    color\n",
       "LITHOLOGY                                          \n",
       "30000            Sandstone         1    ..  #ffff00\n",
       "65030      Sandstone/Shale         2    -.  #ffe119\n",
       "65000                Shale         3    --  #bebebe\n",
       "80000                 Marl         4        #7cfc00\n",
       "74000             Dolomite         5    -/  #8080ff\n",
       "70000            Limestone         6     +  #80ffff\n",
       "70032                Chalk         7    ..  #80ffff\n",
       "88000               Halite         8     x  #7ddfbe\n",
       "86000            Anhydrite         9        #ff80ff\n",
       "99000                 Tuff        10    ||  #ff8c00\n",
       "90000                 Coal        11          black\n",
       "93000             Basement        12    -|  #ef138a"
      ]
     },
     "execution_count": 11,
     "metadata": {},
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   ],
   "source": [
    "df_lith = pd.DataFrame.from_dict(lithology_numbers, orient='index')\n",
    "df_lith.index.name = 'LITHOLOGY'\n",
    "df_lith"
   ]
  },
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   "metadata": {},
   "source": [
    "By just looking at the dictionary, it can be difficult to understand how each of these will look. To solve this, we can quickly create a simple image showing the different hatchings and colors for each lithology. \n",
    "To do this, we will first define some co-ordinates for two new variables x and y. Then we will setup the matplotlib figure and axes using subplots:\n",
    "\n",
    "`fig, axes = plt.subplots(ncols=4, nrows=3, sharex=True, sharey=True, fig size=(10,5), subplot_kw={'xticks':[], 'yticks':[]})`\n",
    "\n",
    "This allows us to define the size of the plot (4 columns by 3 rows), the figure size, and turning off the tick marks on both axes.\n",
    "We then want to loop through the flattened axes and the nested dictionary to add the data and hatched color fills to each subplot. As we are dealing with a nested dictionary, we need to us square brackets multiple times [ ][ ] to go in two levels. The first level is the key, and the second level is the key of the nested dictionary. For example, to retrieve the color we can access it by typing in `lithology_numbers[30000]['color']`\n",
    "\n",
    "This generates a nice looking legend that we can refer to later on. Notice that the lithology symbols do not 100% match the Kansas Geological Survey chart. We will see how to resolve this in a future article."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x360 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = [0, 1]\n",
    "x = [1, 1]\n",
    "\n",
    "fig, axes = plt.subplots(ncols=4,nrows=3, sharex=True, sharey=True,\n",
    "                         figsize=(10,5), subplot_kw={'xticks': [], 'yticks': []})\n",
    "\n",
    "for ax, key in zip(axes.flat, lithology_numbers.keys()):\n",
    "    ax.plot(x, y)\n",
    "    ax.fill_betweenx(y, 0, 1, facecolor=lithology_numbers[key]['color'], hatch=lithology_numbers[key]['hatch'])\n",
    "    ax.set_xlim(0, 0.1)\n",
    "    ax.set_ylim(0, 1)\n",
    "    ax.set_title(str(lithology_numbers[key]['lith']))\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setting up the Well Log Plot With a Lithology Track\n",
    "In previous articles (Enhancing Visualization of Well Logs With Plot Fills and Exploratory Data Analysis With Well Log Data) I have set the plot up on the fly and passed in the dataframe I am working with. In this article, I will be creating a simple makeplot function which we can pass the dataframe into.\n",
    "\n",
    "This allows us to easily reuse the code for other dataframes, assuming the curve names are the same. This could be further refined to make it more generic. This new function takes in three arguments, the dataframe, the top_depth, and the bottom depth.\n",
    "\n",
    "This code will generate a well log plot with 3 tracks one for the Gamma Ray, one containing both Neutron Porosity and Bulk Density, and the final one containing our geological lithology data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def makeplot(well, top_depth, bottom_depth):\n",
    "    fig, ax = plt.subplots(figsize=(15,10))\n",
    "\n",
    "    #Set up the plot axes\n",
    "    ax1 = plt.subplot2grid((1,3), (0,0), rowspan=1, colspan = 1)\n",
    "    ax2 = plt.subplot2grid((1,3), (0,1), rowspan=1, colspan = 1, sharey = ax1)\n",
    "    ax3 = ax2.twiny() #Twins the y-axis for the density track with the neutron track\n",
    "    ax4 = plt.subplot2grid((1,3), (0,2), rowspan=1, colspan = 1, sharey = ax1)\n",
    "\n",
    "    # As our curve scales will be detached from the top of the track,\n",
    "    # this code adds the top border back in without dealing with splines\n",
    "    ax10 = ax1.twiny()\n",
    "    ax10.xaxis.set_visible(False)\n",
    "    ax11 = ax2.twiny()\n",
    "    ax11.xaxis.set_visible(False)\n",
    "    ax13 = ax4.twiny()\n",
    "    ax13.xaxis.set_visible(False)\n",
    "\n",
    "    # Gamma Ray track\n",
    "    ax1.plot(well[\"GR\"], well['DEPTH_MD'], color = \"green\", linewidth = 0.5)\n",
    "    ax1.set_xlabel(\"Gamma\")\n",
    "    ax1.xaxis.label.set_color(\"green\")\n",
    "    ax1.set_xlim(0, 200)\n",
    "    ax1.set_ylabel(\"Depth (m)\")\n",
    "    ax1.tick_params(axis='x', colors=\"green\")\n",
    "    ax1.spines[\"top\"].set_edgecolor(\"green\")\n",
    "    ax1.title.set_color('green')\n",
    "    ax1.set_xticks([0, 50, 100, 150, 200])\n",
    "\n",
    "    # Density track\n",
    "    ax2.plot(well[\"RHOB\"], well['DEPTH_MD'], color = \"red\", linewidth = 0.5)\n",
    "    ax2.set_xlabel(\"Density\")\n",
    "    ax2.set_xlim(1.95, 2.95)\n",
    "    ax2.xaxis.label.set_color(\"red\")\n",
    "    ax2.tick_params(axis='x', colors=\"red\")\n",
    "    ax2.spines[\"top\"].set_edgecolor(\"red\")\n",
    "    ax2.set_xticks([1.95, 2.45, 2.95])\n",
    "\n",
    "    # Neutron track placed ontop of density track\n",
    "    ax3.plot(well[\"NPHI\"], well['DEPTH_MD'], color = \"blue\", linewidth = 0.5)\n",
    "    ax3.set_xlabel('Neutron')\n",
    "    ax3.xaxis.label.set_color(\"blue\")\n",
    "    ax3.set_xlim(0.45, -0.15)\n",
    "    ax3.tick_params(axis='x', colors=\"blue\")\n",
    "    ax3.spines[\"top\"].set_position((\"axes\", 1.08))\n",
    "    ax3.spines[\"top\"].set_visible(True)\n",
    "    ax3.spines[\"top\"].set_edgecolor(\"blue\")\n",
    "    ax3.set_xticks([0.45,  0.15, -0.15])\n",
    "\n",
    "    # Lithology track\n",
    "    ax4.plot(well[\"LITHOLOGY\"], well['DEPTH_MD'], color = \"black\", linewidth = 0.5)\n",
    "    ax4.set_xlabel(\"Lithology\")\n",
    "    ax4.set_xlim(0, 1)\n",
    "    ax4.xaxis.label.set_color(\"black\")\n",
    "    ax4.tick_params(axis='x', colors=\"black\")\n",
    "    ax4.spines[\"top\"].set_edgecolor(\"black\")\n",
    "\n",
    "    for key in lithology_numbers.keys():\n",
    "        color = lithology_numbers[key]['color']\n",
    "        hatch = lithology_numbers[key]['hatch']\n",
    "        ax4.fill_betweenx(well['DEPTH_MD'], 0, well['LITHOLOGY'], where=(well['LITHOLOGY']==key),\n",
    "                         facecolor=color, hatch=hatch)\n",
    "        \n",
    "\n",
    "    ax4.set_xticks([0, 1])\n",
    "\n",
    "    # Common functions for setting up the plot can be extracted into\n",
    "    # a for loop. This saves repeating code.\n",
    "    for ax in [ax1, ax2, ax4]:\n",
    "        ax.set_ylim(bottom_depth, top_depth)\n",
    "        ax.grid(which='major', color='lightgrey', linestyle='-')\n",
    "        ax.xaxis.set_ticks_position(\"top\")\n",
    "        ax.xaxis.set_label_position(\"top\")\n",
    "        ax.spines[\"top\"].set_position((\"axes\", 1.02))\n",
    "        \n",
    "        \n",
    "    for ax in [ax2, ax3, ax4]:\n",
    "        plt.setp(ax.get_yticklabels(), visible = False)\n",
    "        \n",
    "    plt.tight_layout()\n",
    "    fig.subplots_adjust(wspace = 0.15)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The key piece of code to ensure our lithology `fill_betweenx` function from matplotlib creates a variable fill is shown below. All we are doing is looping over each of the keys within the dictionary, assigning simpler variable names to the features within the dictionary, and then using the `fill_betweenx()` function to check if the value in our lithology column equals the dictionary key. If it does, then it will apply the relevant color and hatching to the fill. It will then move on to the next key and repeat until all keys have been cycled through.\n",
    "\n",
    "## Displaying the Well Log Plot & Lithology\n",
    "Once we have the plot set up as a function. We can simply pass in our dataframe and the depths that we want to plot. In this example, we will focus on a small section of the data so that we are able to see the change in the plot fills."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 7 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "makeplot(data, 2975, 3050)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary\n",
    "In this article we have covered how to setup and display lithology data using matplotlib's fill_betweenx() function. We saw how a nested dictionary can be used to store parameters for each lithology and then be called upon using a for loop to fill with the correct color and hatch.\n",
    "Adding this data to a plot can enhance how the geoscientist or petrophysicist relates the electrical logging measurements to the geology.\n",
    "\n",
    "***Thanks for reading!***\n",
    "\n",
    "If you have found this article useful, please feel free to check out my other articles looking at various aspects of Python and well log data. You can also find my code used in this article and others at GitHub.\n",
    "If you want to get in touch you can find me on LinkedIn or at my website.\n",
    "Interested in learning more about python and well log data or petrophysics? Follow me on Medium.\n",
    "## References\n",
    "Kansas Geological Survey, Lithology Symbols. http://www.kgs.ku.edu/PRS/Ozark/PROFILE/HELP/DATA_ENTRY/lithology/Lithology-Symbols.html\n",
    "\n",
    "\"Lithofacies data was provided by the FORCE Machine Learning competition with well logs and seismic 2020\" Bormann P., Aursand P., Dilib F., Dischington P., Manral S. 2020. 2020 FORCE Machine Learning Contest. https://github.com/bolgebrygg/Force-2020-Machine-Learning-competition"
   ]
  },
  {
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