Rahi-1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading and Exploring Log LAS Files With Python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "\n",
    "Log ASCII Standard (LAS) files are a common file format storing and transferring well log data in the oil and gas industry. \n",
    "\n",
    "A LAS file is a file format that was developed by the Canadian Well Logging Society and it is used to store log curve data as well as information about the well.\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.\n",
    "\n",
    "The well data used in this notebook are from the Maui gas field in Offshore Taranaki Basin, New Zealand"
   ]
  },
  {
   "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 library is the most popular python library for reading .las files. More info on the library can be found at: https://lasio.readthedocs.io/en/latest/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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, use the `.read()` method from LASIO. Here, I will be loading Rahi-1 .las file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "las = lasio.read(\"/Users/isaacabrahamodeh/Desktop/Taranaki-wells/Taranaki/rahi-1_final.las\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading and Checking Data\n",
    "In this step we are going to load, check and explore the sections in a typical las file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['Version', 'Well', 'Curves', 'Parameter', 'Other'])"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "las.sections.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[HeaderItem(mnemonic=\"VERS\", unit=\"\", value=\"2.0\", descr=\"CWLS LOG ASCII STANDA\"),\n",
       " HeaderItem(mnemonic=\"WRAP\", unit=\"\", value=\"NO\", descr=\"One line per depth ste\")]"
      ]
     },
     "execution_count": 195,
     "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 especially the header section which contained information 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": 196,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "START DEPTH (STRT): \t\t 136.0932\n",
      "STOP DEPTH (STOP): \t\t 3500.4756\n",
      "STEP (STEP): \t\t 0.1524\n",
      "NULL VALUE (NULL): \t\t -999.25\n",
      "COMPANY (COMP): \t\t \n",
      "WELL (WELL): \t\t Rahi-1\n",
      "FIELD (FLD): \t\t \n",
      "LOCATION (LOC): \t\t \n",
      "COUNTRY (CTRY): \t\t \n",
      "STATE (STAT): \t\t \n",
      "COUNTY (CNTY): \t\t \n",
      "SERVICE COMPANY (SRVC): \t\t Shell Todd Oil Services Limited\n",
      "DATE (DATE): \t\t \n",
      "API NUMBER (API): \t\t \n",
      "UNIQUE WELL ID (UWI): \t\t 100000223430\n",
      "LATITUDE (LATI): \t\t -39.7164729\n",
      "LONGITUDE (LONG): \t\t 173.3434684\n"
     ]
    }
   ],
   "source": [
    "for item in las.sections['Well']:\n",
    "    print(f\"{item.descr} ({item.mnemonic}): \\t\\t {item.value}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Rahi-1'"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "las.well.WELL.value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "by looping through `las.curves`, we can quickly see and print the log curve mnemonic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DEPTH\n",
      "BS\n",
      "CALI\n",
      "DENS\n",
      "DRHO\n",
      "DTC\n",
      "GR\n",
      "NEUT\n",
      "PEF\n",
      "RESD\n",
      "RESD_CORR\n",
      "RESS\n",
      "RESS_CORR\n",
      "SP\n",
      "TEMP\n",
      "TENS\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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Curve: DEPTH, \t Units: M, \t Description: \n",
      "Curve: BS, \t Units: IN, \t Description: Bit Size (Created in Geolog)\n",
      "Curve: CALI, \t Units: IN, \t Description: Caliper Log (CALI) and High resolution Caliper (HCAL)\n",
      "Curve: DENS, \t Units: G/C3, \t Description: Compensated Formation Density Log (RHOB) and (RHOZ)\n",
      "Curve: DRHO, \t Units: G/C3, \t Description: Density Log Correction (DRHO) and Density Log Correction High Res (HDRA)\n",
      "Curve: DTC, \t Units: US/F, \t Description: Delta T (DT)\n",
      "Curve: GR, \t Units: GAPI, \t Description: Environmentally corrected GR log. (ECGR) and Gamma-Ray Log (GR)\n",
      "Curve: NEUT, \t Units: V/V, \t Description: Neutron Porosity (env. corrected and Apha Processed) (NPOR)\n",
      "Curve: PEF, \t Units: B/E, \t Description: Photoelectric Factor (PEF) and (PEFZ)\n",
      "Curve: RESD, \t Units: OHMM, \t Description: Laterolog Deep Resistivity (HLLD) and (LLD)\n",
      "Curve: RESD_CORR, \t Units: OHMM, \t Description: Borehole corrected LLD\n",
      "Curve: RESS, \t Units: OHMM, \t Description: High Res Shallow Laterolog (HLLS) and Shallow Laterolog (HLLS)\n",
      "Curve: RESS_CORR, \t Units: OHMM, \t Description: Borehole corrected LLS\n",
      "Curve: SP, \t Units: MV, \t Description: Spontaneous Potential Log (SP)\n",
      "Curve: TEMP, \t Units: DEGC, \t Description: Measured Temperature (MTEM)\n",
      "Curve: TENS, \t Units: KGF, \t Description: Cable tension at surface (TENS)\n",
      "There are a total of: 16 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": [
    "We can easily be removed the curves we don't need from the LAS file by using the 'las.delete_curve'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [],
   "source": [
    "las.delete_curve('RESD_CORR')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [],
   "source": [
    "las.delete_curve('RESS_CORR')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Curve: DEPTH, \t Units: M, \t Description: \n",
      "Curve: BS, \t Units: IN, \t Description: Bit Size (Created in Geolog)\n",
      "Curve: CALI, \t Units: IN, \t Description: Caliper Log (CALI) and High resolution Caliper (HCAL)\n",
      "Curve: DENS, \t Units: G/C3, \t Description: Compensated Formation Density Log (RHOB) and (RHOZ)\n",
      "Curve: DRHO, \t Units: G/C3, \t Description: Density Log Correction (DRHO) and Density Log Correction High Res (HDRA)\n",
      "Curve: DTC, \t Units: US/F, \t Description: Delta T (DT)\n",
      "Curve: GR, \t Units: GAPI, \t Description: Environmentally corrected GR log. (ECGR) and Gamma-Ray Log (GR)\n",
      "Curve: NEUT, \t Units: V/V, \t Description: Neutron Porosity (env. corrected and Apha Processed) (NPOR)\n",
      "Curve: PEF, \t Units: B/E, \t Description: Photoelectric Factor (PEF) and (PEFZ)\n",
      "Curve: RESD, \t Units: OHMM, \t Description: Laterolog Deep Resistivity (HLLD) and (LLD)\n",
      "Curve: RESS, \t Units: OHMM, \t Description: High Res Shallow Laterolog (HLLS) and Shallow Laterolog (HLLS)\n",
      "Curve: SP, \t Units: MV, \t Description: Spontaneous Potential Log (SP)\n",
      "Curve: TEMP, \t Units: DEGC, \t Description: Measured Temperature (MTEM)\n",
      "Curve: TENS, \t Units: KGF, \t Description: Cable tension at surface (TENS)\n",
      "There are a total of: 14 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": 203,
   "metadata": {},
   "outputs": [],
   "source": [
    "las.write('/Users/isaacabrahamodeh/Desktop/Taranaki-wells/Taranaki/rahi-1_final.las')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Converting LAS File to a Pandas Dataframe\n",
    "LASIO file can be converted to a pandas dataframe using the .df() function.This allow machine learning algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "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": 205,
   "metadata": {},
   "outputs": [
    {
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       "      <th>CALI</th>\n",
       "      <th>DENS</th>\n",
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       "      <th>DTC</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.6615</td>\n",
       "      <td>1041.8923</td>\n",
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       "    <tr>\n",
       "      <th>136.7028</th>\n",
       "      <td>36.0</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",
       "      <td>30.6688</td>\n",
       "      <td>1072.0391</td>\n",
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      ],
      "text/plain": [
       "            BS  CALI  DENS  DRHO  DTC  GR  NEUT  PEF  RESD  RESS  SP     TEMP  \\\n",
       "DEPTH                                                                           \n",
       "136.0932  36.0   NaN   NaN   NaN  NaN NaN   NaN  NaN   NaN   NaN NaN  30.6257   \n",
       "136.2456  36.0   NaN   NaN   NaN  NaN NaN   NaN  NaN   NaN   NaN NaN  30.6382   \n",
       "136.3980  36.0   NaN   NaN   NaN  NaN NaN   NaN  NaN   NaN   NaN NaN  30.6507   \n",
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       "136.7028  36.0   NaN   NaN   NaN  NaN NaN   NaN  NaN   NaN   NaN NaN  30.6688   \n",
       "\n",
       "               TENS  \n",
       "DEPTH                \n",
       "136.0932  1052.5375  \n",
       "136.2456  1044.8145  \n",
       "136.3980  1037.0916  \n",
       "136.5504  1041.8923  \n",
       "136.7028  1072.0391  "
      ]
     },
     "execution_count": 205,
     "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": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BS</th>\n",
       "      <th>CALI</th>\n",
       "      <th>DENS</th>\n",
       "      <th>DRHO</th>\n",
       "      <th>DTC</th>\n",
       "      <th>GR</th>\n",
       "      <th>NEUT</th>\n",
       "      <th>PEF</th>\n",
       "      <th>RESD</th>\n",
       "      <th>RESS</th>\n",
       "      <th>SP</th>\n",
       "      <th>TEMP</th>\n",
       "      <th>TENS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>22077.000000</td>\n",
       "      <td>12047.000000</td>\n",
       "      <td>15771.000000</td>\n",
       "      <td>12034.000000</td>\n",
       "      <td>12014.000000</td>\n",
       "      <td>16076.000000</td>\n",
       "      <td>16083.000000</td>\n",
       "      <td>12021.000000</td>\n",
       "      <td>12015.000000</td>\n",
       "      <td>12008.000000</td>\n",
       "      <td>15994.000000</td>\n",
       "      <td>22064.000000</td>\n",
       "      <td>22027.000000</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>12.943905</td>\n",
       "      <td>10.996209</td>\n",
       "      <td>2.577082</td>\n",
       "      <td>0.013383</td>\n",
       "      <td>93.209797</td>\n",
       "      <td>75.999882</td>\n",
       "      <td>0.297473</td>\n",
       "      <td>3.082521</td>\n",
       "      <td>16.522742</td>\n",
       "      <td>5.928948</td>\n",
       "      <td>-161.847994</td>\n",
       "      <td>63.628516</td>\n",
       "      <td>1743.857836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.019454</td>\n",
       "      <td>1.725533</td>\n",
       "      <td>0.317035</td>\n",
       "      <td>0.025637</td>\n",
       "      <td>15.795433</td>\n",
       "      <td>22.943707</td>\n",
       "      <td>0.240110</td>\n",
       "      <td>0.458740</td>\n",
       "      <td>496.124632</td>\n",
       "      <td>37.745832</td>\n",
       "      <td>16.773844</td>\n",
       "      <td>17.750963</td>\n",
       "      <td>301.865769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>8.500000</td>\n",
       "      <td>8.163700</td>\n",
       "      <td>1.270800</td>\n",
       "      <td>-0.027854</td>\n",
       "      <td>41.216300</td>\n",
       "      <td>22.076900</td>\n",
       "      <td>-0.014900</td>\n",
       "      <td>0.925500</td>\n",
       "      <td>0.944400</td>\n",
       "      <td>0.752400</td>\n",
       "      <td>-188.793100</td>\n",
       "      <td>30.625700</td>\n",
       "      <td>1037.091600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>12.250000</td>\n",
       "      <td>8.666350</td>\n",
       "      <td>2.355000</td>\n",
       "      <td>0.004472</td>\n",
       "      <td>80.587800</td>\n",
       "      <td>55.538000</td>\n",
       "      <td>0.258300</td>\n",
       "      <td>2.860300</td>\n",
       "      <td>1.736600</td>\n",
       "      <td>1.544550</td>\n",
       "      <td>-176.654725</td>\n",
       "      <td>48.664050</td>\n",
       "      <td>1555.794850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>12.250000</td>\n",
       "      <td>12.069100</td>\n",
       "      <td>2.449700</td>\n",
       "      <td>0.008771</td>\n",
       "      <td>92.372550</td>\n",
       "      <td>82.591700</td>\n",
       "      <td>0.307500</td>\n",
       "      <td>3.102900</td>\n",
       "      <td>2.365300</td>\n",
       "      <td>2.199750</td>\n",
       "      <td>-161.910700</td>\n",
       "      <td>58.266550</td>\n",
       "      <td>1795.905800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>16.000000</td>\n",
       "      <td>12.178850</td>\n",
       "      <td>2.626850</td>\n",
       "      <td>0.014006</td>\n",
       "      <td>102.666350</td>\n",
       "      <td>96.107200</td>\n",
       "      <td>0.351500</td>\n",
       "      <td>3.283600</td>\n",
       "      <td>3.905700</td>\n",
       "      <td>3.777700</td>\n",
       "      <td>-150.612975</td>\n",
       "      <td>78.208450</td>\n",
       "      <td>1989.200350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>36.000000</td>\n",
       "      <td>15.598700</td>\n",
       "      <td>3.123600</td>\n",
       "      <td>0.482754</td>\n",
       "      <td>155.844400</td>\n",
       "      <td>163.397600</td>\n",
       "      <td>18.054100</td>\n",
       "      <td>7.412200</td>\n",
       "      <td>31096.597700</td>\n",
       "      <td>1632.735600</td>\n",
       "      <td>-105.289100</td>\n",
       "      <td>101.136900</td>\n",
       "      <td>2498.848400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 BS          CALI          DENS          DRHO           DTC  \\\n",
       "count  22077.000000  12047.000000  15771.000000  12034.000000  12014.000000   \n",
       "mean      12.943905     10.996209      2.577082      0.013383     93.209797   \n",
       "std        4.019454      1.725533      0.317035      0.025637     15.795433   \n",
       "min        8.500000      8.163700      1.270800     -0.027854     41.216300   \n",
       "25%       12.250000      8.666350      2.355000      0.004472     80.587800   \n",
       "50%       12.250000     12.069100      2.449700      0.008771     92.372550   \n",
       "75%       16.000000     12.178850      2.626850      0.014006    102.666350   \n",
       "max       36.000000     15.598700      3.123600      0.482754    155.844400   \n",
       "\n",
       "                 GR          NEUT           PEF          RESD          RESS  \\\n",
       "count  16076.000000  16083.000000  12021.000000  12015.000000  12008.000000   \n",
       "mean      75.999882      0.297473      3.082521     16.522742      5.928948   \n",
       "std       22.943707      0.240110      0.458740    496.124632     37.745832   \n",
       "min       22.076900     -0.014900      0.925500      0.944400      0.752400   \n",
       "25%       55.538000      0.258300      2.860300      1.736600      1.544550   \n",
       "50%       82.591700      0.307500      3.102900      2.365300      2.199750   \n",
       "75%       96.107200      0.351500      3.283600      3.905700      3.777700   \n",
       "max      163.397600     18.054100      7.412200  31096.597700   1632.735600   \n",
       "\n",
       "                 SP          TEMP          TENS  \n",
       "count  15994.000000  22064.000000  22027.000000  \n",
       "mean    -161.847994     63.628516   1743.857836  \n",
       "std       16.773844     17.750963    301.865769  \n",
       "min     -188.793100     30.625700   1037.091600  \n",
       "25%     -176.654725     48.664050   1555.794850  \n",
       "50%     -161.910700     58.266550   1795.905800  \n",
       "75%     -150.612975     78.208450   1989.200350  \n",
       "max     -105.289100    101.136900   2498.848400  "
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "well.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Float64Index: 22077 entries, 136.0932 to 3500.4756\n",
      "Data columns (total 13 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   BS      22077 non-null  float64\n",
      " 1   CALI    12047 non-null  float64\n",
      " 2   DENS    15771 non-null  float64\n",
      " 3   DRHO    12034 non-null  float64\n",
      " 4   DTC     12014 non-null  float64\n",
      " 5   GR      16076 non-null  float64\n",
      " 6   NEUT    16083 non-null  float64\n",
      " 7   PEF     12021 non-null  float64\n",
      " 8   RESD    12015 non-null  float64\n",
      " 9   RESS    12008 non-null  float64\n",
      " 10  SP      15994 non-null  float64\n",
      " 11  TEMP    22064 non-null  float64\n",
      " 12  TENS    22027 non-null  float64\n",
      "dtypes: float64(13)\n",
      "memory usage: 2.4 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": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='DEPTH'>"
      ]
     },
     "execution_count": 208,
     "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.\n",
    "\n",
    "Lets plot individual curves by supplying a y variable argument like so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='DEPTH'>"
      ]
     },
     "execution_count": 209,
     "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": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='DEPTH'>"
      ]
     },
     "execution_count": 210,
     "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='DENS')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='DEPTH'>"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAEGCAYAAABrQF4qAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAdiklEQVR4nO3de3RU5b3/8feXJCTcCSQoECSoiKIiYgSvPdh6Qeqpvfiz0F/rpbW0XdpVemyt1FNLf+3q7diiHvqTQ6uLWi0erZf6U7SiVYEeFAMiF1FugkRuIQEihJDb9/fH7KRDmMllZnKZnc9rrazsefaz9zxPdvLJzDN7P9vcHRERCa8end0AERFpXwp6EZGQU9CLiIScgl5EJOQU9CIiIZfZ2Q2IJS8vzwsLCzu7GSIiaWPlypX73D0/1rouGfSFhYUUFxd3djNERNKGmW2Pt05DNyIiIaegFxEJOQW9iEjIdckxehGRltTU1FBSUkJVVVVnN6VD5eTkUFBQQFZWVqu3UdCLSFoqKSmhX79+FBYWYmad3ZwO4e6UlZVRUlLCqFGjWr2dhm5EJC1VVVUxePDgbhPyAGbG4MGD2/wuRkEvImmrO4V8g0T6rKAXkZjq6p3H39pBXb2mMk93CnoRielPy7dxx5NreOSNuNfhdHtmxu233974+J577mH27NkAzJ49m+HDhzN+/PjGrwMHDrBgwQJuu+22Y/YzefJkiouLmTRpEuPHj+ekk04iPz+/cbtt27Yl1U59GCsiMZVX1gCwv7K6k1vSdWVnZ/PUU08xa9Ys8vLyjlv/3e9+l+9973ut3t+bb74JwIIFCyguLmbu3Lkpaade0YuIJCgzM5MZM2YwZ86czm5Ks1p8RW9mDwHXAHvd/ayg7L+BMUGVgcABdx8fY9ttwMdAHVDr7kUpabWISJSf/L/1vLuzIqX7HDusPz/+1zNbrHfrrbcybtw47rjjjuPWzZkzh0ceeQSA3NxcXn311ZS2sbVaM3SzAJgLPNxQ4O5fbFg2s98AB5vZ/jJ335doA0VEurL+/ftzww03cP/999OrV69j1sUauol31kx7nkHUYtC7+xIzK4y1ziItux74ZIrbJSLSaq155d2eZs6cyYQJE7j55ptbrDt48GD2799/TFl5eXnMMf5USXaM/lJgj7tvirPegZfMbKWZzWhuR2Y2w8yKzay4tLQ0yWaJiHScQYMGcf311/Pggw+2WPf888/nH//4B7t37waguLiYo0ePMmLEiHZrX7Jn3UwHFjaz/mJ332lmQ4DFZvaeuy+JVdHd5wPzAYqKinTiroikldtvv/24s2Six+gBnnnmGQoLC7nvvvuYOnUq9fX19O3bl4ULF9KjR/udG2PuLWdqMHTzXMOHsUFZJvARcJ67l7RiH7OBQ+5+T0t1i4qKXDceEelcv128kftf2cTMy0cz8/LTOrs5x9mwYQNnnHFGZzejU8Tqu5mtjHfCSzL/Qi4H3osX8mbWx8z6NSwDVwLrkng+ERFJQItBb2YLgeXAGDMrMbOvBaum0WTYxsyGmdmi4OEJwDIzewdYATzv7i+mrukiItIarTnrZnqc8ptilO0EpgbLW4FzkmyfiHSyVozudhp373YTm7VmuL0pXRkrIjF19fjMycmhrKwsoeBLVw3z0efk5LRpO811IyIxdfX4LCgooKSkhO52OnbDHabaQkEvIs3qqiMjWVlZbbrLUnemoRsRkZBT0IuIhJyCXkQk5BT0IiIhp6AXEQk5Bb2ISMgp6EVEQk5BLyLN6kYXnoaWgl5EYuqi10lJAhT0IiIhp6AXEQk5Bb2IxKSh+fBQ0ItIs7rqpGbSegp6EZGQU9CLiIRca+4Z+5CZ7TWzdVFls83sIzNbHXxNjbPtFDN738w2m9mdqWy4iIi0Tmte0S8ApsQon+Pu44OvRU1XmlkG8DvgamAsMN3MxibTWBERabsWg97dlwDlCex7IrDZ3be6ezXwGHBtAvsREZEkJDNGf5uZrQmGdnJjrB8O7Ih6XBKUiYhIB0o06B8ATgHGA7uA38SoE+ukrLin5prZDDMrNrPi7nazX5GuTHPdpL+Egt7d97h7nbvXA78nMkzTVAkwIupxAbCzmX3Od/cidy/Kz89PpFkikkI6fT48Egp6Mxsa9fBzwLoY1d4CRpvZKDPrCUwDnk3k+UREJHGZLVUws4XAZCDPzEqAHwOTzWw8kaGYbcA3grrDgD+4+1R3rzWz24C/ARnAQ+6+vj06ISIi8bUY9O4+PUbxg3Hq7gSmRj1eBBx36qWIdH0amg8PXRkrIs3SXDfpT0EvIhJyCnoRkZBT0IuIhJyCXkQk5BT0IiIhp6AXkWZpCoT0p6AXkZh0VmV4KOhFREJOQS8iEnIKehGRkFPQi4iEnIJeRGLSyTbhoaAXkWZpUrP0p6AXEQk5Bb2ISMgp6EVEQk5BLyIScgp6EWmW5rpJfy0GvZk9ZGZ7zWxdVNl/mNl7ZrbGzJ42s4Fxtt1mZmvNbLWZFaew3SLSznSyTXi05hX9AmBKk7LFwFnuPg7YCMxqZvvL3H28uxcl1kQREUlGi0Hv7kuA8iZlL7l7bfDwDaCgHdomIiIpkIox+q8CL8RZ58BLZrbSzGY0txMzm2FmxWZWXFpamoJmiYgIJBn0ZnYXUAs8GqfKxe4+AbgauNXMPhFvX+4+392L3L0oPz8/mWaJiEiUhIPezG4ErgH+t3vsz+XdfWfwfS/wNDAx0ecTEZHEJBT0ZjYF+AHwGXevjFOnj5n1a1gGrgTWxaorIl2PzqoMj9acXrkQWA6MMbMSM/saMBfoBywOTp2cF9QdZmaLgk1PAJaZ2TvACuB5d3+xXXohIu1Gk5qlv8yWKrj79BjFD8apuxOYGixvBc5JqnUiIpI0XRkrIhJyCnoRaZamQEh/CnoRiUlD8+GhoBcRCTkFvYhIyCnoRURCTkEvIhJyCnoRkZBT0IuIhJyCXkQk5BT0IhKTrpMKDwW9iDRLk5qlPwW9iDRLUyCkPwW9iMSkF/LhoaAXEQk5Bb2ISMgp6EVEQk5BLyIScq25Z+xDZrbXzNZFlQ0ys8Vmtin4nhtn2ylm9r6ZbTazO1PZcBERaZ3WvKJfAExpUnYn8Iq7jwZeCR4fw8wygN8BVwNjgelmNjap1oqISJu1GPTuvgQob1J8LfDHYPmPwGdjbDoR2OzuW929Gngs2E5ERDpQomP0J7j7LoDg+5AYdYYDO6IelwRlMZnZDDMrNrPi0tLSBJslIiJNteeHsbGut4h7jZ27z3f3Incvys/Pb8dmiYh0L4kG/R4zGwoQfN8bo04JMCLqcQGwM8HnE5EOppkPwiPRoH8WuDFYvhH4a4w6bwGjzWyUmfUEpgXbiYhIB2rN6ZULgeXAGDMrMbOvAb8ErjCzTcAVwWPMbJiZLQJw91rgNuBvwAbgcXdf3z7dEJFU01w34ZHZUgV3nx5n1adi1N0JTI16vAhYlHDrREQkaboyVkQk5BT0IiIhp6AXEQk5Bb2ISMgp6EVEQk5BLyIScgp6EZGQU9CLiIScgl5EmqU5b9Kfgl5EJOQU9CLSLM15k/4U9CIiIaegFxEJOQW9iEjIKehFREJOQS8iEnIKehGRkFPQi4iEnIJeRCTkEg56MxtjZqujvirMbGaTOpPN7GBUnbuTbrGIiLRJizcHj8fd3wfGA5hZBvAR8HSMqkvd/ZpEn0dEOpfmukl/qRq6+RSwxd23p2h/ItLJTHMfhEaqgn4asDDOugvN7B0ze8HMzoy3AzObYWbFZlZcWlqaomaJSKJcL+VDI+mgN7OewGeAJ2KsXgWMdPdzgP8Enom3H3ef7+5F7l6Un5+fbLNEJEX0wj79peIV/dXAKnff03SFu1e4+6FgeRGQZWZ5KXhOERFppVQE/XTiDNuY2YlmkZE+M5sYPF9ZCp5TRERaKeGzbgDMrDdwBfCNqLJvArj7POA64FtmVgscAaa5a+RPRKQjJRX07l4JDG5SNi9qeS4wN5nnEBGR5OjKWBGRkFPQi4iEnIJeRCTkFPQi0iydPZH+FPQiEpOmQAgPBb2ISMgp6EVEQk5BLyIx6dLG8FDQi0izNFSf/hT0IiIhp6AXEQk5Bb2ISMgp6EVEQk5BLyIScgp6EWmWzrJMfwp6EYlJUyCEh4JeRCTkFPQiIiGXVNCb2TYzW2tmq82sOMZ6M7P7zWyzma0xswnJPJ+IiLRdUveMDVzm7vvirLsaGB18TQIeCL6LiEgHae+hm2uBhz3iDWCgmQ1t5+cUkRTQpGbhkWzQO/CSma00sxkx1g8HdkQ9LgnKjmNmM8ys2MyKS0tLk2yWiKSKTr5Jf8kG/cXuPoHIEM2tZvaJJutj/Y7EfJ3g7vPdvcjdi/Lz85NsloiINEgq6N19Z/B9L/A0MLFJlRJgRNTjAmBnMs8pIiJtk3DQm1kfM+vXsAxcCaxrUu1Z4Ibg7JsLgIPuvivh1oqISJslc9bNCcDTFrl8LhP4s7u/aGbfBHD3ecAiYCqwGagEbk6uuSIi0lYJB727bwXOiVE+L2rZgVsTfQ4R6Xw6+Sb96cpYEYlJc92Eh4JeRCTkFPQiIiGnoBcRCTkFvYhIyCnoRSQmzXUTHgp6EWmWTr5Jfwp6EZGQU9CLiIScgl5EJOQU9CLSLH0mm/4U9CISk6ZACA8FvYhIyCnoRURCTkEvIhJyCnoRkZBT0IuIhJyCXkSap0lv0l4yNwcfYWavmtkGM1tvZt+JUWeymR00s9XB193JNVdERNoqmZuD1wK3u/sqM+sHrDSzxe7+bpN6S939miSeR0Q6k06oT3sJv6J3913uvipY/hjYAAxPVcNERCQ1UjJGb2aFwLnAmzFWX2hm75jZC2Z2ZjP7mGFmxWZWXFpamopmiYgIKQh6M+sLPAnMdPeKJqtXASPd/RzgP4Fn4u3H3ee7e5G7F+Xn5yfbLBERCSQV9GaWRSTkH3X3p5qud/cKdz8ULC8CsswsL5nnFBGRtknmrBsDHgQ2uPtv49Q5MaiHmU0Mnq8s0ecUkU6g0yvTXjJn3VwMfAVYa2arg7IfAicBuPs84DrgW2ZWCxwBprnrt0YkHZhuIhgaCQe9uy+jhdtJuvtcYG6izyEiIsnTlbEiIiGnoBcRCTkFvYhIyCnoRURCTkEvIhJyCnoRkZBT0IuIhJyCXkQk5BT0IiIhp6AXEQk5Bb2ISMgp6EWkWZqFMP0p6EUkpmRuFevu1NTVp64xHai2rp57X97IoaO1nd2UlFHQi0jKff6B/2H0XS9QmyZh7+6s3F6Ou/Pcml3c+/Im7vnb+53drJRR0ItIyr394QEAqmqPD/oVH5Tz+saudV/o59fu4gsPLOcvK0sa34ks+J9tPLdmZye3LDVCF/RVNXXsPljV2c0QabOKqhrS8b487+2uwN2pqqmjvt6ZNn9547rDMYY/rv+v5dz40Io2PcdTq0rYUV4JwDNvf8S1c5dRVVPH/CVbjvmZ7T9c3eK+tpcdpvDO53lzaxk1dfXsPHCE7WWRfX//L2vI6PHPMav7Xt5E+eFqXnt/b5va21r3vryRwjufZ3vZ4XbZf4Nk7jDV5Wzc8zFXzllyTNnd14zl0Te3s6U08oPsmdGDWy4dxbiCgfTNzuRobR0vb9jLxFG51NQ663ce5LPnDqdnZg8OVtZw+xPv8JPPnMn+ymp+8ORaACacNJDbrxxD3+xMVnxQziWj81j70UHOHj6AunrnaG0dfbOzqHfn90u3cvVZQ/n7e3uoOFLL3C+dy/Nrd/GJ0/Kprq0nt3dPDLjy3iVs3nuIyWPy+cMNRZgZW0sP8X9f24IBb2wtY+kPPklGD6Ou3tlfWU1e3+zjfgY1dfW8u7OCcQUDAFiyaR+XnJrH4epasnr0oFfPjJT8rN0dS2YQNw3V1NVz+W9fp67e6WFGTlYPNu45lNC+sjN7cLS2nu9fNYaxw/pTcaSG7zy2unH9kH7Z3HhRIZ8+eyj3vbKJp9/+CICikbnccunJlB+uZnhuL07O68OJA3I4eKSGqfctZe/HRxv38eS3LqT046P8YekH/PzzZzOwVxYHjtQwIrc3W0oPMXJwb/rlZAEEQV3fpt+Pmrp6rpqzhK374ofUp+9fRvG/Xx5z3aoP91N+qJrLx54Qd/vPzF3GlyeN5I4n13Bi/xze+OGnmPnfkZ/T6T96EYB9h6r54dQzuPfljdz78iae+/YlnDV8QNx9Lt8SuZvpF+e/wVcuGMmf3tjOrZed0rg++td6095DTPjpYgDGnNCPZ799MdmZzf+MqmvrycqwVv193PvyJgD+5T9e4/XvT2bk4D4tbpMI64qvIIqKiry4uLjN2xXe+Xw7tEZa8oUJBTy5qiShbYcP7MX5hbm8u6uCs4YNYPG7e/j4aC3/dsVp/P29vfTKymD51sgfZtHIXOrdGdQnm3p36t157f3IEMCnzx7K82t3HbPvmy8u5JOnD+Hk/L6sLTnINx9Z2bhu4qhBZGf2YOmmfcz78nnUuzMitzd17ry0fjcvrtvNkP7ZzPnieE7sn8NL7+7hG39aSdi9/v3J9M3O5M9vfshvFm88Zt25Jw3k0Vsm0btnJpf86u+U7D/Sqn1+9/LT+M7loxsfN/073fbLTwORD0EPV9cxoNc///mMmrXouLpNt2/Yf0P5r78wjuvPHxG3PY8X7+COv6w5pqwh8AF698ygsrou5raj8vrw5Lcuagz/H//rWG66qJCausgLvH9sLuObj6zktstO5XtXjYnbhgZN+/LoLZO4+NS8FreLxcxWuntRzHVhCfpYvxQi0rGuO6+AH10zlgG9so4JsRV3fYrePTPpm515XLj9+euT2FJ6mGWbSvnb+j0MHZDD49+4kEt//epx+//h1NP5+aL3jim7a+oZXDt+GBN//goAP732TL5yYSEVVTXMWbyRM4cN4BOn5TGkXw4QGfppeFfQ4HPnDm9819RWS++4LGZb//3TZ3DLpSfH3W7hig+Z9dTaY8ry+2Xz1l2x3wG1pN2C3symAPcBGcAf3P2XTdZbsH4qUAnc5O6rWtpvoq/oG9TXO4era6mvh3P+z0tMnziCX3x+XOP6D8sq6ZuTSU5WD3plZVCy/wj3v7KJz507nC/94U0+dfoQfnXdOHYdqKK6rp4Pyw/zuXMLGrevrK7lja1lvLB2N0+sLOG9n07BDH723AYKcntRsv8I0yaOYPSQfmwrO8y2fYc5v3AQfXMyqaqp48FlH9DDjBsvLGT51n08t2YXP5x6BtmZPVi2eR/feWw1j94yiYLcXvTNzuTv7+3lgpMH8/rGUp4o3kFBbm8OHKnmpotGsb3sMG9/eICSA0e4bEw+b394gE+ePoSC3F587Y///BneP/1cikbmctEv/84ZQ/uzYVdFm36mg/v0pKwV458AF548mHUfHeTjOKen5fXtyb5DrdtXVzPr6tP5+qUn8/rGUnZXVLG/sppfv/g+L868lNNP7E/54Wpye2dhZry7s4JThvQhOzODI9V1xwyL/O7Vzew+WMUdUyJDgB8frSUnM4OemZGPzQ4freXx4h1cM24YvXpm0Dc7Mso687G3eWb1Tr58wUn87LNnU1VTxz827+OcEQMZ3KfnMcMFZYeOsqfiKGOH9W/cZ++gDbsrqjhQWcPid/cwbeIIHn9rB/e8dOwr+Nb40TVj+ZfT8jl1SN+Y6/Uuu+0a3uG0VbsEvZllABuBK4AS4C1guru/G1VnKvBtIkE/CbjP3Se1tO9kgz7ajvJKThyQQ1ZG6z53PlhZQ6+e//yDk85RWV1Lr6yMNn8OUF1bz9qPDnLeyNw2P+eeiioG9+lJZvC70vAu8YqxJ/D7G2L+/TTW66jPK6pr6yk/XM2JA3LaZf97Kqp4Y2sZ144fHvf5t5cdpqKqhvNGDmpxf7OeWsvCFR+mupkp8dnxwyjM69M4Tt5VdLWgvxCY7e5XBY9nAbj7L6Lq/BfwmrsvDB6/D0x2910xdtkolUEvkoz9h6vpk52pf/xJeHNrGcMG9qKqpo5ReX3Ysf8I+f2yG9+lFG8r57p5/zxT5+GvTmRL6SFuuqjwuH+gT60q4e6/rmf13Vc0/kOur3fG/vhFZl19Bl88fwS/X7KVA0dqeHDZB3zlgpG8sG43F5w8iF98/mxeWr+Hh5dv45FbJjV+EN2gvt5Z+NaHPFFcwj3/axzbyyo5a/gADlTWcOqQvhyqqqXOneVbyjihfzY5WRlMm//GcRdWPXhjEQN7Z3HeyEHsOniEHz2zjpc37OW68woYPaQvaz86yCdPH8LOA0f40qSRlB8+ygf7Kvn6w8Xcfc1YvnrJqIR+zu0V9NcBU9z9luDxV4BJ7n5bVJ3ngF+6+7Lg8SvAD9z9uBQ3sxnADICTTjrpvO3btyfULhFJP+/sOMBTq0q46NQ8rjrzxM5uTkIqq2sx7Lgzl9ydZZv3cdEpececuplqzQV9MqdXxmpx0/8arakTKXSfD8yHyCv6JNolImnmnBEDOWfEwM5uRlJ694wdp2bGpaPzO7g1x0rm/WgJEH0OUwHQ9DKy1tQREZF2lEzQvwWMNrNRZtYTmAY826TOs8ANFnEBcLCl8XkREUmthIdu3L3WzG4D/kbk9MqH3H29mX0zWD8PWETkjJvNRE6vvDn5JouISFskNQWCuy8iEubRZfOilh24NZnnEBGR5OicMRGRkFPQi4iEnIJeRCTkFPQiIiHXJWevNLNSoDWXxuYB+9q5OV2R+t29qN/dS6L9HunuMa/M6pJB31pmVhzvkt8wU7+7F/W7e2mPfmvoRkQk5BT0IiIhl+5BP7+zG9BJ1O/uRf3uXlLe77QeoxcRkZal+yt6ERFpgYJeRCTkulTQm9lDZrbXzNZFlQ0ys8Vmtin4nhu1bpaZbTaz983sqqjy88xsbbDufuuoG3omKE6/Z5vZR2a2OviaGrUuLP0eYWavmtkGM1tvZt8JykN9zJvpd6iPuZnlmNkKM3sn6PdPgvKwH+94/e644+3uXeYL+AQwAVgXVfZr4M5g+U7gV8HyWOAdIBsYBWwBMoJ1K4ALidzh6gXg6s7uWwL9ng18L0bdMPV7KDAhWO5H5GbzY8N+zJvpd6iPedDGvsFyFvAmcEE3ON7x+t1hx7tLvaJ39yVAeZPia4E/Bst/BD4bVf6Yux919w+IzHk/0cyGAv3dfblHfjIPR23TJcXpdzxh6vcud18VLH8MbACGE/Jj3ky/4wlLv93dDwUPs4IvJ/zHO16/40l5v7tU0Mdxggd3pQq+DwnKhwM7ouqVBGXDg+Wm5enoNjNbEwztNLydDWW/zawQOJfIq51uc8yb9BtCfszNLMPMVgN7gcXu3i2Od5x+Qwcd73QI+nji3Xi81Tck7+IeAE4BxgO7gN8E5aHrt5n1BZ4EZrp7RXNVY5Slbd9j9Dv0x9zd69x9PJH7R080s7OaqR72fnfY8U6HoN8TvGUh+L43KI934/GSYLlpeVpx9z3BL0c98HtgYrAqVP02sywiYfeouz8VFIf+mMfqd3c55gDufgB4DZhCNzjeDaL73ZHHOx2C/lngxmD5RuCvUeXTzCzbzEYBo4EVwVu/j83sguAT6RuitkkbDb/4gc8BDWfkhKbfQTsfBDa4+2+jVoX6mMfrd9iPuZnlm9nAYLkXcDnwHuE/3jH73aHHu7M+iY71BSwk8hamhsh/r68Bg4FXgE3B90FR9e8i8on0+0R9+gwUBT+0LcBcgiuAu+pXnH7/CVgLrAkO/NAQ9vsSIm891wCrg6+pYT/mzfQ71MccGAe8HfRvHXB3UB724x2v3x12vDUFgohIyKXD0I2IiCRBQS8iEnIKehGRkFPQi4iEnIJeRCTkFPTSrZhZXTBT4PpgNsF/M7MewbrJZnYwajbB1WZ2eZPt1pnZE2Y2PKrO7iazEPY0s0NNnvcmM5vbGX0WyezsBoh0sCMeuRQdMxsC/BkYAPw4WL/U3a9pYbtHgS9GPZ4NHHL3exoqt3b2WJGOoFf00m25+15gBpGJpdqSzEuBU9unVSKpp1f00q25+9Zg6KZhxsRLg1kGG3zB3bc0PDCzTOBq4MUWdt2ryX4GEbn6UaTDKehFjp0VMN7QTXRwLyUyV01zGod6IDJGT+TydZEOp6CXbs3MTgbqiMyYeEYzVY8JbpF0ojF66bbMLB+YB8x1TfokIaZX9NLdNAzBZAG1RGYQjJ4iuekY/c/c/S8d1zyR1NPslSIiIaehGxGRkFPQi4iEnIJeRCTkFPQiIiGnoBcRCTkFvYhIyCnoRURC7v8DpBMid0XUbFcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "well.plot(y='NEUT')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='DEPTH'>"
      ]
     },
     "execution_count": 212,
     "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='TEMP')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='DEPTH'>"
      ]
     },
     "execution_count": 213,
     "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='BS')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='DEPTH'>"
      ]
     },
     "execution_count": 214,
     "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='DTC')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='DEPTH'>"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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aWcwSlAY7BgALeb/ziHVDADCgE5HlwrUVT5Q6z/KqapSUlVuybj7ggohcpaSsotY0O3LH0fhTMOkfazB46gJLtuGxcT8Z0InIEcqEnqJmpAEAvt1ebN02LFtzbQzoJliWfxRLdlp3QhC5nZvbm4feidwxfaVl22JAN8Gdf/sed81Y5XQy6r1f/iMPP/vb904ngxzgVOlLpGKfPUdP4195+03YRvD76jAdrcyQatmak8Sx07XLBqlu5m2xfrxoSiyJnDG/+Y2lOFVehdtzOsW1Hpah1yN2Hiy7FZ08hxUFJU4ng1wq0X86p0waI8bCDHktDOhxSuQcRrxuemMpxr9rXXkfJTcV9IgL7XSdaRYFRTtirf5eWoMBncIqOmVNu1wiIyqqPZi9Ov4ybKexHXo94pYH2RJZSe9nolTkO9wPvt9nWXpqEmHDJliGTkRulwhF6HakgWXo9Qjz50TRxZJJTYQepFrxpoetXOoRlrgQRadf5GJndaE+WwbnYg69/hDm0Ymi0gtq4Z4pqvt5i0K/kbUm2A1DRIYCuoiMFpHtIpIvIlN05vcWkRUiUi4ivzc/mfY6V1mNNxbuREWVx+mkELlCuIBsNFg2cPBWON54rncXcNSikR2jBnQRSQHwFoDrAfQBMF5E+oQsdgzAgwBeMj2FDpixdDdenr8Ds1bscTopRK6gm0NXNYE++KEWtYN3A4viuVO57xlLd1uyXiM59CEA8pVSu5RSFQBmAxijXUApVaSUWg3Aukdx2OhMhbeH2LnKaodTkhgSrZKK6p9wFYM1kyNHbCeLNg+eOBvX5/X23Kq9MRLQOwLQtu4v9E2LmYhMFJE8EckrLnbH6ITOV+tYj/Gc4pXaQC/U1JxYQTl0vRVYlUM38Pt9/PPN8W1DZxNWFSEZCei6D+auy8aUUtOVUjlKqZy2bdvWZRUJJ/Rg7ThyCl2nzMXektPOJMgCjOcUr/TU2mEklvHQrcrRGsmsWJGhsaoIyUhALwSgHW4sC8BBa5JT/4Qe64/XFAIAvtp82P7EWIRFLhSvsJ1rfOdWtPhmVY7WyGrjrhTVWYNVPcyNBPTVAHqKSLaIpAMYB2COJalxETeFQDftCzkjXLNF/+Ro8c2qRi525FX0tmHV/kQdD10pVSUikwHMA5ACYKZSaouITPLNnyYi7QHkAWgGwCMiDwPoo5Q6aU2yrRVPrzb/gXJTptZN+0LO0MulxnJemRUAz1RUBT3DwFg7dPN/AFbdcRh6wIVSKhdAbsi0aZrXh+EtinEVI7dFoYfaXxvvpspSN+0LOSPe4G1WAJwwYxXy9h43ZV3xcLIMnerATblaN+0LOUOv2aKCsv3cqhXMbdi+Xv2Bk2XoSedMhfH256EnJMd2IaotbMci34z3V9YMlZtoP6GjZfE+ZlKvUjTOVYbBgK7jveV7AACHS89FXVZbHFF8qhzvLCqwKlmOYQ6dzBJaHm242aJFEdBIceK2QycxZ4O5DfucbIeetErPGuj4qjkfvv2hqGZyhChYUlaOszHcBTiNZegUr3A5dKNjhRceP2NugmK0evexOn+2R7umtaaxDN0B5VWxBd00nc4TegZPXYBb31lelyQ5gjl0itfB0rOYOCsPpzUZGQUFj8GI/uJX2y1Jl9FzO55MTavGabWmOdrKJVlVGxhsUXuYtQcp2omy9VD9adHJeE7xevGrH1BQfBq5Gw8FTa/W+aEkw2MdWSnqACPtT5Mh98qeohQvvTNIKaDazuez6aUh9L0F57puxyLTt+LFgB6BXu4hkvLKmiy9w+epqVy0K+QU5f8X/Wwymnm1M6Nh9qZYhm6jgZ1bAABGXnhe1GW1J6j2dbiLQZWRcpwEwww6xSvc8LktGnnLlwd3aRnzOs04L2u1urHgXNcdbdGiiM6ArqNzq0YAgCYNo1cxhD0BwszYe8zZ2vo6YUCnOPlPIe3PwtsO3fu6S+tGgelGQ52dD182G8vQE5T2lNIWs5yuqMaBOAfGTxRstkhmMTR2ionrClpe5wLQsWWmJduK9lmWodvI/yzR+VuPxPS5Kk1En7F0N4Y+/41paZq78RD2HHVmjPV6nBGiBOE/h4Jy6HFmFWI9L7UZrqt7twMAjB0YPASVXae6oT4udcCAruOgr4fo3E2HoiwZfNV/LnebZWl64IO1uPav31m2/kgYzyle/uKRoDonBd2obDT3GuvlwOkWNVorCkosWS8Dup4YLv3aRY2MAZMSR9lZZbUzJySbLVK89HLoQJxFGTHn0PUGCAtdpxXNFjmWi61OnqsM6hVq5iEtPlUe9F7b+uXFr34wcUvWSYRwvrfkNJblH3U6GWQibQY96CHQBqOdNk6+v3Jv1Ie6B1fIxnZWn6usDhTFmiGFrVys0++przF++srA+0hf9ardx3DXjO8NNz+85M8Lgt5rb/vericDedmdQd9XUrsl0JV/WYQ7//a9vQkh0/gDqDaDo5SKK0esLXJ57LPNeGX+jojL67aKMThY2KdrD+DmN5bi2+1FuGvG94bT/fDsdfg/H2+sNZ2Dc1ls7b4T+GzdAVRUedC9bZOwyz08ex2W7DyKI74TM9bzMZHK8YwKLassPWNNhY5ffvEpS9dP9vOfQa8t3Kk7vU7rDPlw0Un90VF3FZdhw/4TOKhpdaZC/vv9+K1lYbe3/cgp3PP31Viy8yhKTgcPqVvtUbhrxvdYuSu4bPyz9fqjNLJjkQ0e/nA9ej32JT5ddyDsMikp3iPhz6HXt4qZoc9/gyte/Da2D2mSvHDbEfR/5utaJ66fx6OQX1QWRwrja6NbVe3B4h3FcW2fzBeu/DpQ5FKHQx66Rr3g+f2uElz98ncY89YyXPvXxTWf9X1468GT6DplbmD6loMn8cjHG/D+yr0Rt50zdQEOldZcIIpPlWPJzqN4aPY6Q2lnDj1B+A9EXeNyXQK6mRU1B06cxb5jZ7B233EUnYo+3jsQ/MPxB/KNhSd0l52+ZBdGvvIdNh8ojbrec5XVuuWekU71rlPm4sSZ8A8ceOObfEyYuQpLd9Yub1+ys9jxYViTTaS4tf/YmcC59fGawsD0JQYvyEY6FhUUR27qu1Az5LXfR3mFeOyzzVHXfflz3+DUueC7VaM/VQZ0gwqKy2xpleE/HLFuqqoOAd2KTP3Yt5fjpteXGlo2tHcfAJyr9ARO5tKzlXho9jqUnq3EWt8jvgqPR+9U1e+pr9H/6a9rTY+WQx/wzHws3XkUo/66GKfLq4Lm7Snx/oCPlpXX+txdM1Zh5CvONP1Mdnq/k4dmr9f9rX5tsP9HtN/eZ+sO1Aq4ft+ZdBfnf65B4OHwmnlfRmj23MCiyOuagF5Z7cHQ57/BNS9/F3S1j+arzdHbmmvF2+wq2jjor8zfgXcX7wqaZlUX56JTtYOeHn+x0nc7ivF339OcXpm/Axc/5Q3GM5buxufrD+Lvy3ZHXM/7K/fih8M1wwZXVHtQrtNywEje5Wczvsf2I6fwj5Bb42hf1blK/cpspRSey92GXcXxFRdRMP+xLNa5wALxVbiXhVzMtX44fBIPf7gez31pT0uy0Aze8oKj+NU/14Zfnjn0yFYUlAS62m8ycLvvN+n98F96JHUJskaeUvT6wp34s6+D0uYDpaio8hgOvPE4droiqCdqUDNO367+fOaqWkVGhcfPoFLT4ifSt/LYZ5sx+tUlUdMSy7nuT095VbVubs/jUZi+uCDijx/w3lH838W7cPXL3+nm7qlu/IEr3M/l3xHqqyI5XHouYk/sWJ4LHBcJ+e9zMkpPUD7gIoq0lJprk5WD9vhzq4Gebwa35Q0su8LOzy8qw4qCmnLfvSWncdMbtYtEPB4FkdpX+KKT59AsMw0ZaSmG0hNqxF++xclzVdjz/I0AgHMVNUF604FSdGihP+bFsBdqKli1X0Us5+vRsnK0adJQdz3RVHsUzlVWo/fjX+G/R3QPTH/4w/W4JLsVthwoxbO5P2B3mGETik6ew7EzFWicXvNTWJZ/FGMGdDSeCALgPTcf+WQjfn55V1yc1dzQZ4w+6OXhkT2D3oerC6mq9iA1pYHtTW0/X+etkK3JDET+AbCVSxT7NQfYjoYk/m0Y3dTcTYfw1wXh28mOeXMpHv98S+D9H/+tXykz4qVFGPSn+UHT1u07jiHPLsQtb+sX5xw7XYGPVu/XnbfXV+Z88lxwDlZ7UXzr2/yw6Q7nQEgZeqRHjeVMXRBU1hnLBXnhD0WBcvO3FxUEXUiGPv8NJv5jDQBg8Q79TknDXvgWo19dEjScqdMtkeqrvcfO4OM1hbh/1urANLPi1okzlVi47QgWbD2CrlPmhq2jeTbXX8RizzH8cJX3d7VqT/AzR/OLIje9TbWoEN01OfTZq/YFXht5TuG8LYfjOtmqPQr/ytuv22lAT6TilspqT9CzFgFgaZhekft0ht+duWwPAO/TyfU88M+1WLGrBCt3125q+GzuNhwqrWnt8sTnmzHuks5o3zwjbHoj8cfiZ77YinuHZePgibPYWFgK7Q/s1LnKWsMY+MvjtesAgKJT5/C7jzaE3d6G/SeCinHCHVO9kS+Pna5Aha+4SJtj+nhNIcYOyqq1PEX21BxvhuTIyZoiq7o0AtDz3vI9eM9XfwMAG8K0svp2exGeuLmPKds04uX5O3BT/w5BQ3oUnyrHS19H7uSUkcaAHnD7tOVo2SgdI/uchzV7juOF2/oF5aqM5LB+6cu5RTP4T/Ox4tFrkJ4afACuf20JGqUbK96YMHMVhnZvHXa+XhO7aJRSmvLJmv2tqPLgk7WF+PGAjsj0pW+Fr6nhp2trl1fO2xLcomDWir2YtWIv/nBD75jTFOpoWTl+pFPOqQ3eeqo8Cu8u3oUb+52PyR+sxdp9JwxvM5bKJu2djrbr+XKLBk5yu/2azMbSnUfxsxnW9ez9uy8TE8p/FFftPm7ZtkNd9dKioPdGhs2ua9FoNPUyoK/e4z1Y/uZNL9zWLyjgVmgq6QqKyyAAukXo/RlJyekK7Cw6hRtfX4oXb+sXNM9oxcviHcVhO7vsPnoa97y3WndeJA/NXo+Xbu+PtBTBF5oH7/7o+W9wtKwcH+Xtxzt3DsZlzy2Med2A9tYV2FhYivX7T0T9TLVHBVWQ5kxdEGHp8H4xKw8AApXDsTAazr/5IfhCVtfviWpUemqOfV2OnSnEW77+goPjJEXqber3yyu7WbLtehnQ9dxw8fmBQP/5+oPIbtMYD4/shWte9rY73vP8jVi5qwQXtm+G73bG1gZ15xFvU7Zn/rMVzTPTTE136NXdqDkbDmLOhoO4bXBw0YC/UmbdvhOmBikjJ+mbdShrN1ukXr5+P5m2olaZZ6iKKk+tuzIKb8vBUuw/VpMzDVf8Z7WTZ6uCKuoTVevGDaMvVAeuCOgzl+7GM19sDZr26oKdQWWxE2flGe6wEOrhD9cD8LZ7jdb8zW6xtLknr2jBHABmr96HCZd3tT4xLnGjwU5qVqsvTU452mIEocHcTzsQUF2DOSUnbTEWUX1hKKCLyGgR2S4i+SIyRWe+iMjrvvkbRWSQ+UklshFbLlI9FDWgi0gKgLcAXA+gD4DxIhLaLuh6AD19fxMBvGNyOgOMNEkkildDi5qVEVnJyFk7BEC+UmqXUqoCwGwAY0KWGQNglvJaCaCFiJxvcloB2FNm/Ltre8W0/L1Ds3WnN22oX0Wx8HdXYvyQTmHXl/fYSIy4oG1MabDL2EHBPShzHxwe1MszXg0EeO+eSzC6b/vAtN+MrDkeP/I1/0xPaYC5Dw4Lu549z9+I18YNqHM6Rmm2T1RfGAnoHQFouxkW+qbFugxEZKKI5IlIXnFx3UY723Y4fO15l9aNDK3jg/svxbrHr0W3to115//6mp6607Vm3p2D/713CGZPvKxWRwZ/sP7JJd7/9w7NxsUda7pCd2/bBHdc0ll3vb+8ohvaNGmI9+4Zgt3P3YBZ9w4BAGS1zMTmp0fh1TsG6H5u5IXnYd7DV+CKXm2x6o/X4L5h+heZL349DH+9o3+t6f6geV2f8yLsNfDUf/UNvL5tcBb6dGiGvMdGollGKkb1rf3ZJY9chSnX98agzi0ABB+jFY9ejdfHD8TXv7kCu569AWMHdcTsiZdjxAXtMO2uwZh5dw5eHz8QD/m6fQ/q3AIf/OIybH56FLb9aTT6dtDvXn6rr1NQaJv0/xltvG39jRdbkh8hspSRVi561bGh5R5GloFSajqA6QCQk5NTp7KTX13ZPWyngklXdsejn26Kuo4f9WgDALi5Xwe8tnAn/ntEd9x5WRc8NWcLdh4J32V32ZSrkdpA0DQjFY3Sw3919wzNxnNj+6GiyoNe5zXB7YM74XRFFS5+6utA7n9ApxaYde8QTJi5CgDw++t64c5Lu6BFo5pmkSKCy7q1Rt8OzfDo9ReiScNU/HhgRwzr2QYLth7BbYOz8O6S3ThcehYPXN0D7ZpmBC4Aj9/UB8N7tsGUTzbhsO9JLp8/MBQXdWyOizo2x28+DO59+dDInnhoZE8syz+Kr7cewaDOLbB23wk0Sk/BvyZdjtxNh7Bm73E0y0jDtJ8NwqT312L8kJqL0sanRgHwDmFwXd/2yC8qw08v7YxOrRph0pXdMenKmnFW3llUgIGdW+D85pn4r/41Y8S88pMBQWm6unfNBWLnn68PDGjURHPnc1m3Vli56xgy01Jw1je2+nNjLwYAtNA0Mf3md1eiW9smgfbJq/84MvB4wNfHD8Tx0xV40tfTcViPNkHHgezXt0MzbDlobdPH/+rfAXM2eMdg2fXsDVi3/0TY0VDf/OlATP7A+/CKkReehwXbjDeyePLmPnj6PzUNN3Y/d0McqY7MSEAvBKAtH8gCEPpoECPLmKJds4zAjzhUeor+Dcc/77808DzKgmdrvkx/D8uGqSno2CIT707ICcz7cOJlWLDtCN5d4h0StnlmGjqGGaAqdFu9zmvqTU9qg0BOvGlGWmDgK78retUUq0y+Wv+uID21AeY+ODxoWpsmDTHOF0x/pRmQKtSIC9ph5R+uwbTvCvDmN/no36lFYF6nVplB7Yb9hvZogw1PXoeUBoLH/r0JT9zcF60apwflhkdfdH6tffH7fHL4YhC/SGkOJy3MsX1j/CBc8ucFaNwwBVf0aoN5W44g1dckbHjPNnht3ACMvqg9GqZ6e+ZtePI6bNh/Am2bNsQXvx6GuZsO4eZ+50NEcHtOFk6erarzsAfJLCOtge7QxOc3zwgaWiKc58dejCmazNjcB4cHPUkoklsHZeGTtcaKYnO6tESeb8z+tk1rigobNBAM7tISEy7vglkraj+tKC2lAT751eUoPH4Wo/q2R+/HvwIAfDzpclzUsXngfahdz96ABg0EjdNT8cgn3mFCrBo6FzAW0FcD6Cki2QAOABgH4Kchy8wBMFlEZgO4FECpUsqydl/TJ+SgoKgMe0pOY83e43h/pXccl5aN07B8ytW1upsP7dEGH/ziUrTITA9q/1ntC+h6TUIv7dYax89UBAL6G+MHRkxTu6YNMe6SThjqy/0b9dkDQ9G6cXpMn4lVaA4ZAJY8cjUueOxLlFd50Cpk+/7OU6+Oi7zPiaBZpvcUvn94N9wztCtKyioCA22JSK1RE5tnpgUupP67Fb9G6ZHvvCi8tY9fiz5PzAPgvUPyKIURF7RDxxaZGPr8N0Hd4W8bnFWrLuyOSzph7KAs9HrsS5zXzBtoH7+pD/75/V7s8j116KXb++PgibN4Zf4OdGiegYO+C0W4x0DedVkX9GjXJHDntfWZUYFBsVIbCJ7X6U36u+suwHnNMvCXedsBAEOyW2HVbm/mcXCXVhjcJXj5nK6tIn4v/nMxUlGxmaKevUqpKhGZDGAegBQAM5VSW0Rkkm/+NAC5AG4AkA/gDIB7rEsy0CwjDQM7t8TAzi0xqm97nN88E60bp+OqC9pBRJD74HDsO3YGzTPTAiPx/ah77UB72+BOmLViL348UH+o1FF92+Otnw7CqL7nITVMDtFv1R9H1mlfBmhyzXZb+/i1KD5VjlZNrL2gWKlhakrQ3UK4YX7JWv4LYeP0lKCiOAC4pGtLHFh/Fgt+eyWqPB70atcUf77lIjRMTQnkwkUE6amCLU+PCmS67huWjfuGZWP0q4sxtEebQK/oB311XB+u3of/+WRT4I7Mr13Thvj1NT1x55DOgZz3svyjtS7Wep17mmem4YGremBwl5ZoIIJ3l3iHvI51ON6re7fDEzfV1K0ZeYKXGQxlR5RSufAGbe20aZrXCsAD5ibNmEbpqXjgqh5B0/p0aIY+HZoBAC6PMChWdpvG2OQr+9UjIrixn3srxxo3TEXjMC1xiGL1yk/6Y1DnlrWmPzv2Ytye0wk92tWMp9SwQc3gVL3bNw281jsfv3r4Ct3t+etUQkd0/PcDQ4OKR0PvxPyGdm+DdxYV6K77sm7euNEoPQVbD57E5d3CxxGtdk0bouhUOWbefUnQ9PFDOmH+1iMY3jO2O/hY8ddMRKYIN+Rwo/TUsEWRuQ8OR8eWdburEp0Hea76wzVo18xYHYi/CWykIHtRx+ZYNuVqw2la/MhVuuP5N8vwFmN2aG7tHSQDOhE5xn8nXReBYaM1JSdGgzngLd9e8Nsr0aFF/JXgv7u2F/aUnAk7LG5O11Z4bdwAXNfH2v4NDOhEVC8NyfZWSI4dmIXiU+VYUofnCmiLgeJhpO+KHY81ZEAnonqpS+vGgQrxnK4tcex0hcMpch4HrCCiei8jLYUtnMAcOhFRzF68tV/YoUOcxIBORBQj/zhNiYZFLkRELsGATkTkEgzoREQuwYBOROQSDOhERC7BgE5E5BIM6ERELsGATkTkEqJiHbndrA2LFAOo/ayn2toAiH3UnfqP+51cuN/JJZ797qKUaqs3w7GAbpSI5CmlcqIv6S7c7+TC/U4uVu03i1yIiFyCAZ2IyCXqQ0Cf7nQCHML9Ti7c7+RiyX4nfBk6EREZUx9y6EREZIAjAV1EZopIkYhs1kxrJSLzRWSn739LzbxHRSRfRLaLyCjN9MEissk373UJPAY88YTZ56dE5ICIrPf93aCZV+/3GQBEpJOIfCsi20Rki4g85Jvu9uMdbr9dfcxFJENEVonIBt9+P+2b7vbjHW6/7T3eSinb/wBcAWAQgM2aaS8CmOJ7PQXAC77XfQBsANAQQDaAAgApvnmrAFwO73O/vwRwvRP7E8c+PwXg9zrLumKffek9H8Ag3+umAHb49s/txzvcfrv6mPvS2MT3Og3A9wAuS4LjHW6/bT3ejuTQlVKLARwLmTwGwP/6Xv8vgB9rps9WSpUrpXYDyAcwRETOB9BMKbVCeb+FWZrPJJww+xyOK/YZAJRSh5RSa32vTwHYBqAj3H+8w+13OG7Zb6WUKvO9TfP9Kbj/eIfb73As2e9EKkM/Tyl1CPD+GAC0803vCGC/ZrlC37SOvteh0+ubySKy0Vck478NdeU+i0hXAAPhzb0kzfEO2W/A5cdcRFJEZD2AIgDzlVJJcbzD7Ddg4/FOpIAejl75kYowvT55B0B3AAMAHALwsm+66/ZZRJoA+ATAw0qpk5EW1ZlWb/ddZ79df8yVUtVKqQEAsuDNdV4UYXG377etxzuRAvoR3+0GfP+LfNMLAWifyJoF4KBvepbO9HpDKXXEdxJ4ALwLYIhvlqv2WUTS4A1q/1RKfeqb7PrjrbffyXLMAUApdQLAIgCjkQTH20+733Yf70QK6HMA/Nz3+ucAPtdMHyciDUUkG0BPAKt8t22nROQyXy3wBM1n6gX/Ce5zCwB/CxjX7LMvnTMAbFNKvaKZ5erjHW6/3X7MRaStiLTwvc4EMBLAD3D/8dbdb9uPt0M1wv8P3tuPSnivSPcBaA1gIYCdvv+tNMv/Ed5a4O3Q1PgCyPF9QQUA3oSvo1Qi/oXZ538A2ARgo+8An++mffaldxi8t4wbAaz3/d2QBMc73H67+pgD6AdgnW//NgN4wjfd7cc73H7berzZU5SIyCUSqciFiIjiwIBOROQSDOhERC7BgE5E5BIM6ERELsGATq4kItW+0e22+EbA+62INPDNGyEipZoR8NaLyMiQz20WkX+JSEfNModDRs5LF5GykO3eLSJvOrHPRKlOJ4DIImeVtxs2RKQdgA8ANAfwpG/+EqXUTVE+908Ad2jePwWgTCn1kn/hWEY2JbIac+jkekqpIgAT4R0kKZYIvARAD2tSRWQ+5tApKSildvmKXPyj/A33jYznd6tSqsD/RkRSAVwP4Ksoq84MWU8reHsEEtmOAZ2SiTZ3Hq7IRRugl8A7HkskgSIawFuGDm/XbSLbMaBTUhCRbgCq4R3l78IIiwYFaKL6hGXo5Hoi0hbANABvKg5eRC7GHDq5lb/oJA1AFbyj3mmH7w0tQ5+qlPrYvuQRmY+jLRIRuQSLXIiIXIIBnYjIJRjQiYhcggGdiMglGNCJiFyCAZ2IyCUY0ImIXIIBnYjIJf4/aj4VGLBBehIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "well.plot(y='DRHO')"
   ]
  },
  {
   "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}