01 - Loading and Exploring Log LAS Files.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading and Exploring Log LAS Files With Python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The accompanying video for this notebook can be found on my YouTube channel at:  \n",
    "Please check it out, like and subscribe.\n",
    "\n",
    "## Introduction\n",
    "\n",
    "Log ASCII Standard (LAS) files are a common Oil & Gas industry format for storing and transferring well log data. \n",
    "\n",
    "A LAS file is a structured file format that was developed in the late 1980s and early 1990s by the Canadian Well Logging Society and it is used to store log curve data as well as information about the well. The format makes it simple to transfer well log data between people and it is easy to explore these files as they are stored within plain text.\n",
    "\n",
    "This notebook illustrates how to load data in from a LAS file and carry out a basic QC of the data before plotting it on a log plot."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading and Checking Data\n",
    "The first step is to import the required libraries: pandas, matplotlib and LASIO.  \n",
    "lasio is a library that has been developed to handle and work with LAS files. More info on the library can be found at: https://lasio.readthedocs.io/en/latest/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import lasio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To load our file in, we can use the `.read()` method from LASIO like so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "las = lasio.read(\"Data/15-9-19_SR_COMP.LAS\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['Version', 'Well', 'Curves', 'Parameter', 'Other'])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "las.sections.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[HeaderItem(mnemonic=\"VERS\", unit=\"\", value=\"2.0\", descr=\"CWLS Log ASCII Stan...),\n",
       " HeaderItem(mnemonic=\"WRAP\", unit=\"\", value=\"NO\", descr=\"One line per depth s...)]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "las.sections['Version']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that our file has been loaded, we can start investigating it's contents.\n",
    "\n",
    "To find information out about where the file originated from, such as the well name, location and what the depth range of the file covers, we can create a simple for loop to go over each header item. Using Python's f-string we can join the items together."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top Depth (STRT): \t\t 102.1568\n",
      "Bottom Depth (STOP): \t\t 4636.514\n",
      "Depth Increment (STEP): \t\t 0.1524\n",
      "Null Value (NULL): \t\t -999.25\n",
      "Field Name (FLD): \t\t Q15\n",
      "NAME (WELL): \t\t 15/9-19\n",
      "WELLBORE (WBN): \t\t 15/9-19 SR\n",
      "COUNTRY (NATI): \t\t NOR\n",
      "COUNTRY (CTRY): \t\t NOR\n",
      "OPERATOR (COMP): \t\t STATOIL\n",
      "PERM DATUM (PDAT): \t\t MSL\n",
      "RIG NAME (COUN): \t\t NORTH SEA\n",
      "STATE (STAT): \t\t NORWAY\n",
      "PB WELL ID (PBWE): \t\t 15/9-19\n",
      "PB WELLBORE ID (APIN): \t\t 15/9-19 SR\n",
      "PB WELL NAME SET (PBWS): \t\t ALL\n"
     ]
    }
   ],
   "source": [
    "for item in las.sections['Well']:\n",
    "    print(f\"{item.descr} ({item.mnemonic}): \\t\\t {item.value}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see above that we have the key information about the well, such as the name and location.\n",
    "\n",
    "If we wanted to change one of these entries, for example, COUNTRY (CTRY), we can easily do that by using the following line of code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "las.sections['Well']['CTRY'] = 'Norway'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now see that if we run the code again the COUNTRY value is updated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top Depth (STRT): \t\t 102.1568\n",
      "Bottom Depth (STOP): \t\t 4636.514\n",
      "Depth Increment (STEP): \t\t 0.1524\n",
      "Null Value (NULL): \t\t -999.25\n",
      "Field Name (FLD): \t\t Q15\n",
      "NAME (WELL): \t\t 15/9-19\n",
      "WELLBORE (WBN): \t\t 15/9-19 SR\n",
      "COUNTRY (NATI): \t\t NOR\n",
      "COUNTRY (CTRY): \t\t Norway\n",
      "OPERATOR (COMP): \t\t STATOIL\n",
      "PERM DATUM (PDAT): \t\t MSL\n",
      "RIG NAME (COUN): \t\t NORTH SEA\n",
      "STATE (STAT): \t\t NORWAY\n",
      "PB WELL ID (PBWE): \t\t 15/9-19\n",
      "PB WELLBORE ID (APIN): \t\t 15/9-19 SR\n",
      "PB WELL NAME SET (PBWS): \t\t ALL\n"
     ]
    }
   ],
   "source": [
    "for item in las.sections['Well']:\n",
    "    print(f\"{item.descr} ({item.mnemonic}): \\t\\t {item.value}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also call upon the sections in a different way.\n",
    "In this case we can use `las.well` to call upon the well section.\n",
    "\n",
    "If we just want to extract the Well Name, we can simply call it by using the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'15/9-19'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "las.well.WELL.value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To quickly see what curve mnemonics are present within the las file we can loop through `las.curves` and print the mnemonic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DEPT\n",
      "AC\n",
      "CALI\n",
      "DEN\n",
      "GR\n",
      "NEU\n",
      "RDEP\n",
      "RMED\n"
     ]
    }
   ],
   "source": [
    "for curve in las.curves:\n",
    "    print(curve.mnemonic)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To see what curves are present within the las file, we can repeat the process with the Curve Item object and call upon the `unit` and `descr` functions to get info on the units and the curve's description.\n",
    "The enumerate function allows us to keep a count of the number of curves that are present within the file. As enumerate returns a 0 on the first loop, we need to 1 to it if we want to include the depth curve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Curve: DEPT, \t Units: M, \t Description: 1  DEPTH\n",
      "Curve: AC, \t Units: US/F, \t Description: 2  Sonic Transit Time (Slowness)\n",
      "Curve: CALI, \t Units: IN, \t Description: 3  Caliper\n",
      "Curve: DEN, \t Units: G/CC, \t Description: 4  Bulk Density\n",
      "Curve: GR, \t Units: GAPI, \t Description: 5  Gamma Ray\n",
      "Curve: NEU, \t Units: %, \t Description: 6  Neutron Porosity\n",
      "Curve: RDEP, \t Units: OHMM, \t Description: 7  Deep Resistivity\n",
      "Curve: RMED, \t Units: OHMM, \t Description: 8  Medium Resistivity\n",
      "There are a total of: 8 curves present within this file\n"
     ]
    }
   ],
   "source": [
    "for count, curve in enumerate(las.curves):\n",
    "    print(f\"Curve: {curve.mnemonic}, \\t Units: {curve.unit}, \\t Description: {curve.descr}\")\n",
    "print(f\"There are a total of: {count+1} curves present within this file\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Curves can easily be removed from the LAS file. For example, if we do not whish to retain the RMED curve, we can remove it like so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "las.delete_curve('RMED')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Curve: DEPT, \t Units: M, \t Description: 1  DEPTH\n",
      "Curve: AC, \t Units: US/F, \t Description: 2  Sonic Transit Time (Slowness)\n",
      "Curve: CALI, \t Units: IN, \t Description: 3  Caliper\n",
      "Curve: DEN, \t Units: G/CC, \t Description: 4  Bulk Density\n",
      "Curve: GR, \t Units: GAPI, \t Description: 5  Gamma Ray\n",
      "Curve: NEU, \t Units: %, \t Description: 6  Neutron Porosity\n",
      "Curve: RDEP, \t Units: OHMM, \t Description: 7  Deep Resistivity\n",
      "There are a total of: 7 curves present within this file\n"
     ]
    }
   ],
   "source": [
    "for count, curve in enumerate(las.curves):\n",
    "    print(f\"Curve: {curve.mnemonic}, \\t Units: {curve.unit}, \\t Description: {curve.descr}\")\n",
    "print(f\"There are a total of: {count+1} curves present within this file\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "las.write('Exports/15_9-19_Exported.las')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Converting LAS File to a Pandas Dataframe\n",
    "Data loaded in using LASIO can be converted to a pandas dataframe using the .df() function. This allows us to easily plot data and pass it into one of the many machine learning algorithms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "well = las.df()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `.head()` function generates a table view of the header and the first 5 rows within the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AC</th>\n",
       "      <th>CALI</th>\n",
       "      <th>DEN</th>\n",
       "      <th>GR</th>\n",
       "      <th>NEU</th>\n",
       "      <th>RDEP</th>\n",
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       "    <tr>\n",
       "      <th>DEPT</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
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       "      <th>102.1568</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.3274</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>102.3092</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>5.8235</td>\n",
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       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102.4616</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.5228</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102.6140</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.2285</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102.7664</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.5020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          AC  CALI  DEN      GR  NEU  RDEP\n",
       "DEPT                                      \n",
       "102.1568 NaN   NaN  NaN  5.3274  NaN   NaN\n",
       "102.3092 NaN   NaN  NaN  5.8235  NaN   NaN\n",
       "102.4616 NaN   NaN  NaN  6.5228  NaN   NaN\n",
       "102.6140 NaN   NaN  NaN  7.2285  NaN   NaN\n",
       "102.7664 NaN   NaN  NaN  9.5020  NaN   NaN"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "well.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To find out more information about data, we can call upon the `.info()` and `.describe()` functions.  \n",
    "    \n",
    "The `.info()` function provides information about the data types and how many non-null values are present within each curve.  \n",
    "The `.describe()` function, provides statistical information about each curve and can be a useful QC for each curve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AC</th>\n",
       "      <th>CALI</th>\n",
       "      <th>DEN</th>\n",
       "      <th>GR</th>\n",
       "      <th>NEU</th>\n",
       "      <th>RDEP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7007.000000</td>\n",
       "      <td>7007.000000</td>\n",
       "      <td>7084.000000</td>\n",
       "      <td>28117.000000</td>\n",
       "      <td>7096.000000</td>\n",
       "      <td>12223.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>79.498714</td>\n",
       "      <td>9.611068</td>\n",
       "      <td>2.449055</td>\n",
       "      <td>71.986798</td>\n",
       "      <td>20.117166</td>\n",
       "      <td>2.438382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>18.390705</td>\n",
       "      <td>1.019751</td>\n",
       "      <td>0.165122</td>\n",
       "      <td>32.922780</td>\n",
       "      <td>14.729530</td>\n",
       "      <td>6.631202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.025100</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.943000</td>\n",
       "      <td>2.766100</td>\n",
       "      <td>2.178300</td>\n",
       "      <td>0.250300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>66.154350</td>\n",
       "      <td>9.142900</td>\n",
       "      <td>2.319975</td>\n",
       "      <td>50.255200</td>\n",
       "      <td>10.631350</td>\n",
       "      <td>0.988000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>74.661900</td>\n",
       "      <td>9.589400</td>\n",
       "      <td>2.505600</td>\n",
       "      <td>74.998500</td>\n",
       "      <td>15.718550</td>\n",
       "      <td>1.536100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>93.304550</td>\n",
       "      <td>9.904800</td>\n",
       "      <td>2.585125</td>\n",
       "      <td>92.343800</td>\n",
       "      <td>25.067375</td>\n",
       "      <td>2.434050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>181.813900</td>\n",
       "      <td>20.330400</td>\n",
       "      <td>3.001300</td>\n",
       "      <td>304.333700</td>\n",
       "      <td>146.347400</td>\n",
       "      <td>198.537100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                AC         CALI          DEN            GR          NEU  \\\n",
       "count  7007.000000  7007.000000  7084.000000  28117.000000  7096.000000   \n",
       "mean     79.498714     9.611068     2.449055     71.986798    20.117166   \n",
       "std      18.390705     1.019751     0.165122     32.922780    14.729530   \n",
       "min       1.025100     6.000000     1.943000      2.766100     2.178300   \n",
       "25%      66.154350     9.142900     2.319975     50.255200    10.631350   \n",
       "50%      74.661900     9.589400     2.505600     74.998500    15.718550   \n",
       "75%      93.304550     9.904800     2.585125     92.343800    25.067375   \n",
       "max     181.813900    20.330400     3.001300    304.333700   146.347400   \n",
       "\n",
       "               RDEP  \n",
       "count  12223.000000  \n",
       "mean       2.438382  \n",
       "std        6.631202  \n",
       "min        0.250300  \n",
       "25%        0.988000  \n",
       "50%        1.536100  \n",
       "75%        2.434050  \n",
       "max      198.537100  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "well.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Float64Index: 29754 entries, 102.1568 to 4636.514\n",
      "Data columns (total 6 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   AC      7007 non-null   float64\n",
      " 1   CALI    7007 non-null   float64\n",
      " 2   DEN     7084 non-null   float64\n",
      " 3   GR      28117 non-null  float64\n",
      " 4   NEU     7096 non-null   float64\n",
      " 5   RDEP    12223 non-null  float64\n",
      "dtypes: float64(6)\n",
      "memory usage: 1.6 MB\n"
     ]
    }
   ],
   "source": [
    "well.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quick Plot\n",
    "\n",
    "Using the ploting function within pandas, we can plot all curves on a single plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='DEPT'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "well.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When we look at the plot above, we can see that is not very useful to us as all logging measurements have their own scales. We can see that RDEP, which is Deep Resistivity is very flat. That is becuase it is notmally plotted on a logarithmich scale. Similary, density (DEN) is also not easily distinguisable due to its small range (1.5 g/cc to 3g/cc).\n",
    "\n",
    "We can plot individual curves by supplying a y variable argument like so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='DEPT'>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "well.plot(y='GR')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This allows us to view the Gamma Ray Curve on its own. We can change the variable to any other curve within the dataframe and view it as follows: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='DEPT'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "well.plot(y='DEN')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}