Maui-3.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 Maui-3 .las file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "las = lasio.read(\"/Users/isaacabrahamodeh/Desktop/Taranaki-wells/Taranaki/maui-3_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": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['Version', 'Well', 'Curves', 'Parameter', 'Other'])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "las.sections.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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": 61,
     "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": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "START DEPTH (STRT): \t\t 457.5048\n",
      "STOP DEPTH (STOP): \t\t 3400.806\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 Maui-3\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 BP & Todd Oil Services Ltd\n",
      "DATE (DATE): \t\t \n",
      "API NUMBER (API): \t\t \n",
      "UNIQUE WELL ID (UWI): \t\t 100000022053\n",
      "LATITUDE (LATI): \t\t -39.5346325\n",
      "LONGITUDE (LONG): \t\t 173.4515882\n"
     ]
    }
   ],
   "source": [
    "for item in las.sections['Well']:\n",
    "    print(f\"{item.descr} ({item.mnemonic}): \\t\\t {item.value}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Maui-3'"
      ]
     },
     "execution_count": 63,
     "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": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DEPTH\n",
      "BS\n",
      "CALI\n",
      "DENS\n",
      "DRHO\n",
      "DTC\n",
      "GR\n",
      "GR_CORR\n",
      "NEUT\n",
      "NEUT_CORR\n",
      "RESD\n",
      "RESD_CORR\n",
      "RESS\n",
      "RESS_CORR\n",
      "SP\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": 65,
   "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: Converted from Geolog version 4. (CALI)\n",
      "Curve: DENS, \t Units: G/C3, \t Description: Converted from Geolog version 4. (RHOB)\n",
      "Curve: DRHO, \t Units: G/C3, \t Description: Converted from Geolog version 4. (DRHO)\n",
      "Curve: DTC, \t Units: US/F, \t Description: Converted from Geolog version 4. (BHC)\n",
      "Curve: GR, \t Units: GAPI, \t Description: Converted from Geolog version 4. (GRXB)\n",
      "Curve: GR_CORR, \t Units: GAPI, \t Description: Gamma ray corrected\n",
      "Curve: NEUT, \t Units: V/V, \t Description: Converted from Geolog version 4. (SNP)\n",
      "Curve: NEUT_CORR, \t Units: V/V, \t Description: Corrected NPHI (limestone units)\n",
      "Curve: RESD, \t Units: OHMM, \t Description: Converted from Geolog version 4. (ILD)\n",
      "Curve: RESD_CORR, \t Units: OHMM, \t Description: Borehole corrected ILD\n",
      "Curve: RESS, \t Units: OHMM, \t Description: Converted from Geolog version 4. (L7)\n",
      "Curve: RESS_CORR, \t Units: OHMM, \t Description: Borehole corrected LL7\n",
      "Curve: SP, \t Units: MV, \t Description: Converted from Geolog version 4. (SP)\n",
      "There are a total of: 15 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": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "las.delete_curve('RESS_CORR')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "las.delete_curve('GR_CORR')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "las.delete_curve('NEUT_CORR')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "las.delete_curve('RESD_CORR')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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: Converted from Geolog version 4. (CALI)\n",
      "Curve: DENS, \t Units: G/C3, \t Description: Converted from Geolog version 4. (RHOB)\n",
      "Curve: DRHO, \t Units: G/C3, \t Description: Converted from Geolog version 4. (DRHO)\n",
      "Curve: DTC, \t Units: US/F, \t Description: Converted from Geolog version 4. (BHC)\n",
      "Curve: GR, \t Units: GAPI, \t Description: Converted from Geolog version 4. (GRXB)\n",
      "Curve: NEUT, \t Units: V/V, \t Description: Converted from Geolog version 4. (SNP)\n",
      "Curve: RESD, \t Units: OHMM, \t Description: Converted from Geolog version 4. (ILD)\n",
      "Curve: RESS, \t Units: OHMM, \t Description: Converted from Geolog version 4. (L7)\n",
      "Curve: SP, \t Units: MV, \t Description: Converted from Geolog version 4. (SP)\n",
      "There are a total of: 11 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": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "las.write('/Users/isaacabrahamodeh/Desktop/Taranaki-wells/Taranaki/maui-1_raw.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": 75,
   "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": 76,
   "metadata": {},
   "outputs": [
    {
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       "  <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>RESD</th>\n",
       "      <th>RESS</th>\n",
       "      <th>SP</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEPTH</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>457.5048</th>\n",
       "      <td>23.0</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>-9.3210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>457.6572</th>\n",
       "      <td>23.0</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>-9.6083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>457.8096</th>\n",
       "      <td>23.0</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>-9.8956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>457.9620</th>\n",
       "      <td>23.0</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>-10.1829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458.1144</th>\n",
       "      <td>23.0</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>-10.6346</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            BS  CALI  DENS  DRHO  DTC  GR  NEUT  RESD  RESS       SP\n",
       "DEPTH                                                               \n",
       "457.5048  23.0   NaN   NaN   NaN  NaN NaN   NaN   NaN   NaN  -9.3210\n",
       "457.6572  23.0   NaN   NaN   NaN  NaN NaN   NaN   NaN   NaN  -9.6083\n",
       "457.8096  23.0   NaN   NaN   NaN  NaN NaN   NaN   NaN   NaN  -9.8956\n",
       "457.9620  23.0   NaN   NaN   NaN  NaN NaN   NaN   NaN   NaN -10.1829\n",
       "458.1144  23.0   NaN   NaN   NaN  NaN NaN   NaN   NaN   NaN -10.6346"
      ]
     },
     "execution_count": 76,
     "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": 77,
   "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>RESD</th>\n",
       "      <th>RESS</th>\n",
       "      <th>SP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>19314.000000</td>\n",
       "      <td>19163.000000</td>\n",
       "      <td>3323.000000</td>\n",
       "      <td>3367.000000</td>\n",
       "      <td>19058.000000</td>\n",
       "      <td>19075.000000</td>\n",
       "      <td>3376.000000</td>\n",
       "      <td>19078.000000</td>\n",
       "      <td>2845.000000</td>\n",
       "      <td>19180.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>10.306952</td>\n",
       "      <td>10.498308</td>\n",
       "      <td>2.381516</td>\n",
       "      <td>0.033016</td>\n",
       "      <td>102.600420</td>\n",
       "      <td>61.232783</td>\n",
       "      <td>0.200030</td>\n",
       "      <td>6.069443</td>\n",
       "      <td>19.874493</td>\n",
       "      <td>-38.837640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.035413</td>\n",
       "      <td>1.877065</td>\n",
       "      <td>0.131372</td>\n",
       "      <td>0.048217</td>\n",
       "      <td>25.098971</td>\n",
       "      <td>12.801250</td>\n",
       "      <td>0.062472</td>\n",
       "      <td>72.817195</td>\n",
       "      <td>30.143372</td>\n",
       "      <td>30.630306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>8.500000</td>\n",
       "      <td>6.918100</td>\n",
       "      <td>2.088100</td>\n",
       "      <td>-0.023777</td>\n",
       "      <td>56.537200</td>\n",
       "      <td>19.231700</td>\n",
       "      <td>0.057746</td>\n",
       "      <td>0.621100</td>\n",
       "      <td>1.218700</td>\n",
       "      <td>-118.816300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8.500000</td>\n",
       "      <td>8.700200</td>\n",
       "      <td>2.271450</td>\n",
       "      <td>0.008578</td>\n",
       "      <td>82.801050</td>\n",
       "      <td>54.542600</td>\n",
       "      <td>0.161472</td>\n",
       "      <td>1.339725</td>\n",
       "      <td>6.186300</td>\n",
       "      <td>-56.736575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8.500000</td>\n",
       "      <td>9.889500</td>\n",
       "      <td>2.347000</td>\n",
       "      <td>0.018568</td>\n",
       "      <td>93.977650</td>\n",
       "      <td>64.586800</td>\n",
       "      <td>0.196677</td>\n",
       "      <td>2.161800</td>\n",
       "      <td>10.103100</td>\n",
       "      <td>-28.327350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>12.250000</td>\n",
       "      <td>12.265250</td>\n",
       "      <td>2.506050</td>\n",
       "      <td>0.031351</td>\n",
       "      <td>119.887375</td>\n",
       "      <td>70.025650</td>\n",
       "      <td>0.222884</td>\n",
       "      <td>3.716200</td>\n",
       "      <td>21.478300</td>\n",
       "      <td>-18.059475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>23.000000</td>\n",
       "      <td>15.053500</td>\n",
       "      <td>2.781200</td>\n",
       "      <td>0.220435</td>\n",
       "      <td>195.558300</td>\n",
       "      <td>161.199900</td>\n",
       "      <td>0.413137</td>\n",
       "      <td>3741.387900</td>\n",
       "      <td>303.746600</td>\n",
       "      <td>21.352300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 BS          CALI         DENS         DRHO           DTC  \\\n",
       "count  19314.000000  19163.000000  3323.000000  3367.000000  19058.000000   \n",
       "mean      10.306952     10.498308     2.381516     0.033016    102.600420   \n",
       "std        2.035413      1.877065     0.131372     0.048217     25.098971   \n",
       "min        8.500000      6.918100     2.088100    -0.023777     56.537200   \n",
       "25%        8.500000      8.700200     2.271450     0.008578     82.801050   \n",
       "50%        8.500000      9.889500     2.347000     0.018568     93.977650   \n",
       "75%       12.250000     12.265250     2.506050     0.031351    119.887375   \n",
       "max       23.000000     15.053500     2.781200     0.220435    195.558300   \n",
       "\n",
       "                 GR         NEUT          RESD         RESS            SP  \n",
       "count  19075.000000  3376.000000  19078.000000  2845.000000  19180.000000  \n",
       "mean      61.232783     0.200030      6.069443    19.874493    -38.837640  \n",
       "std       12.801250     0.062472     72.817195    30.143372     30.630306  \n",
       "min       19.231700     0.057746      0.621100     1.218700   -118.816300  \n",
       "25%       54.542600     0.161472      1.339725     6.186300    -56.736575  \n",
       "50%       64.586800     0.196677      2.161800    10.103100    -28.327350  \n",
       "75%       70.025650     0.222884      3.716200    21.478300    -18.059475  \n",
       "max      161.199900     0.413137   3741.387900   303.746600     21.352300  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "well.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Float64Index: 19314 entries, 457.5048 to 3400.806\n",
      "Data columns (total 10 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   BS      19314 non-null  float64\n",
      " 1   CALI    19163 non-null  float64\n",
      " 2   DENS    3323 non-null   float64\n",
      " 3   DRHO    3367 non-null   float64\n",
      " 4   DTC     19058 non-null  float64\n",
      " 5   GR      19075 non-null  float64\n",
      " 6   NEUT    3376 non-null   float64\n",
      " 7   RESD    19078 non-null  float64\n",
      " 8   RESS    2845 non-null   float64\n",
      " 9   SP      19180 non-null  float64\n",
      "dtypes: float64(10)\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": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='DEPTH'>"
      ]
     },
     "execution_count": 79,
     "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": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='DEPTH'>"
      ]
     },
     "execution_count": 80,
     "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": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='DEPTH'>"
      ]
     },
     "execution_count": 81,
     "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": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='DEPTH'>"
      ]
     },
     "execution_count": 82,
     "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='NEUT')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='DEPTH'>"
      ]
     },
     "execution_count": 83,
     "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='SP')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='DEPTH'>"
      ]
     },
     "execution_count": 84,
     "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": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='DEPTH'>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dntfudYlzYkhdHS13nNEYoFRmijoIGGOWG2NaG2MKjTGFuG78Q4wxO4B3gEkiUk9EugI9ga8cLbENsT5Af/qbUUG32wkCwY5pWZBn+7Mvmv5lzbmsW/Kzn21i0Xd7Q73FEZ4goFUBpTKSnSGiLwP/A3qLSImIXBXqWGPMSuA1YBUwC7i+tkcGQezNQaFG99i5Pb769eaAbfm52dx2Th/bn3/atI+AmlnHc9eU8v0nvrD9/li4R1IlWwh4acFmduw/FvlApVRc7IwOutgY084Yk2uM6WiMecZvf6ExZpfX71ONMd2NMb2NMe8notCRxNOU3qxB4HBSO0/JM/4X2CmcLULvto1sf/bWfUe5esbXAdv/u3QblYlanMZTE0jM6WNRdrCc2/+znB//s9YrkUplHJ0x7OehH0buBLZdDom+VjJndWnAtl++/A2Pzl3Hc59vcnyRGnfpkigGUFntCnh7j4RftU0pFb+0XE8gntFBTj4RiziXyu7Rj9YDsOdIBZPH9nLorDX/r7RPQKnMpDUBP+5b4eg+rbngxKBTHGzLEueHeT46dx3LSvYBrhv3M59tYl8cT8yemoDGAKUyUnoGgRD33acuH+rz+8XDOgcc434iduLenZ0lDO/WnIuHdYp8cBTmr9vF7JU7eHfZdu59dxV/eHdVzOeqy4yrSqm6l57NQSHqAuP6t2Vol2aeYZd9wnbaxt+Uc/f3+pOTncX9Fw7k5a+cW2zmkbnrfLKVuieXxaJmdJBWBZTKRBlVEwDfpqJgxxmvfSd2bgpAt1YNoy5Do/wcWjfKj3hcLOf2T1d9qLwy6nO4SRKODlJK1Z60DAJ2Bc1xYWr2XTa8Cx//+kyGdmke9bnt9gVcfXq3qM/tb87qUs8s42hpn4BSmS0tg0C4+2/ke13NESJC15bRP6mHK8Pb15/mef3mdac61l/w36XxZefQ5iClMlN6BoEwrfk/KurodWDgcZ6aQJwdAv5vz812benulU9oSOdmIYez/v68flF9XnWsj/LaMaxURkvPIBDmxnbRSZ0ZYrX1h8vP5vRi9TlZof9Xt20c2Hdw1eldozt/dhaH4+gbSMbmIKe/A6VUoPQMAhH2u2/IwW4yTt0Ls/1u+jlWTSBY2/0b150a9+fd+PI39L/rg6jfl6y5g5RStSM9g4ADM4bjbQ7yX9f41WtP4ZoRXWmcHzgqt23jfPq2axzfB0Zp96FySvYeScp5AslYK1EqXaVnEIjjAKeGTPq/v1/7xtwxoZ8nQHlPVMvOEp64dEh8H2jZfajc1nFFU+dw+p8+rtmQoBvv/zbsZsDdH3DgWEViPkApFZf0DAIxPN12aFrf+YKEUDxtAvdfOMBnW7gy/25CX9vnXrvzoK3j3EHq8mcW2D53LC5++ksOHqtk1bYDCf0cpVRs0jQIhI8CN43uSV5OFn3buWYMd2han49/faajZThvYLuojm9aP/QCNB+s3GH7PFkiPh3EP/nnV/T9/ayQx1dU1U7bSyzzGJKxqUqpdJOWaSMiOa1HS7794zkcr6xmaJdm/HZ8H/JynI2Hvx1vfzEZgCYNcnnnhtP43mOfB+zbts/+4irXvbiYPYePc8uYXnRr1ZCP15bZel+i5wm0CTICKhTtElCq9mRkEHDLy8nijV/EPzInmKwY1gcOVRuIJkDtOezKKPqXOd9G/fmJ0K5JPtv3Hws7HNefJ4lfgsqklKqRls1B8ajLG0+oCV/+/Qfeiro0c+Szvy7em9A1BaI5c80ILQ0DSiWaBoEEuONc+x253po1CF4TGN6tRcj3vG6zJjPji+KI6w54L2pfUVXN9E83xL2SWU1uIm3kUSoZ2Vlo/lkRKRWRFV7bHhSRNSKyTET+IyJNvfbdJiLrRWStiIxLULkTri5y6TQJsr5xKKd2b8EfJva3ffxd76zklleX8N3uw6zYuj/oMeWV1Z6b9X++2cp9763hiXkbbH9GMO6n+TiyXSulEshOTeA5YLzftg+BE4wxA4FvgdsARKQfMAnob73ncRHJdqy0KeIihxeR8fYra2nJPm0bc8UphVG99+O1ZZzx4DzO+9tnQfdf+o8FdL3tPQbdM5tDx1wjjJxa5zea3EZb9h4BIEymDaWUQyL+MzPGfArs8ds22xjjHof4JeDOyjYReMUYU26M2QSsB4Y5WN6kN6RzUxrn23+i99c0Qm2gQT1XX773TfWKU7oAwXMQxWL/0QrPamVOteJUV0c+xu2Sp11zFyoqtfqgVKI58az1U+B963UHwHsJrRJrW8qItS/yTivrZ7y3rfm3jmLR78aE3G+lIPIJApee7AoCiehHrayuZv/R2Gb7Fk6ZydZ9R4E4spwqpRIqriAgIncAlcCL7k1BDgv6r19ErhWRhSKysKzM3lj2ZDaoU1NHztMoP5cWBfU8v/sPD3UPPfW+qbpv/okYS/PyV1sYdM/suM+jMUCp5BTzPAERuRI4DxhtaoZ+lADeDeIdgaCrnRhjpgPTAYqKilL+FpGIZRq/vG10QCI69/m9Vy5zv3JySGVutvjMJj5WUUV+bmD3zsFjFQy42xUkNt53bsj5EVoTUCo5xVQTEJHxwG+B7xljjnjtegeYJCL1RKQr0BP4Kv5iJj/PUEgHz9m2ST5NvYaN9mnbKOhEqkSMp/dPJ9HHL/VERZWrkX/XoZqO4+NVNQ3/ZQd9E9lpCFAqOdkZIvoy8D+gt4iUiMhVwGNAI+BDEVkiIk8CGGNWAq8Bq4BZwPXGmPgGmteRaB9cJcErtq+5dzz//eXp1M9zPY27O4hdn52Qjwyw84ArfcWsFdvpecf7rNlxgLe+2Rr0WP9cQbHUBHTJS6USL2JzkDHm4iCbnwlz/FRgajyFqlux3VHbNHa145/Rq5WThfFwN8V8f0hHdh067rPyWG3Nqz35vrkUT5vA3NWlACzbst/n6d/7Pu9/A/efLGaM4Z+fF3PhkA4+tR1vOrdAqcTL6NxBTmrXpD4Lbh9NK69O3UTIyc7i+lE9fLa5ayG1USMonDKT7w9xjQi+9Y1lPvu8b/z+D/7+N/TFm/fxh3dX8XXxHp64bGjQz9JZxkolnk7H8dPDWgj+rD6to35vm8b5MSWOi1dNx7C947u1bMicyWdwtVdt4sIT7Y/kfWNxSdDt1T41Ab99flGgvNLVSrjvSOjhp1oTUCrxtCbgp0frApbffTYF9VLnf020NYCsLKFH6wKys2vemO1A8PJ+cve/6fvf0GvWNg59p9cRRUolXurc6WpRozhm/NaFYMNGw6n2jDByttbiUxPwu3/7N+3Y6Uf3DyRKKeelbXPQPd/rzxu/OMWx8/VoXcC955/gsy2aZR8TyX1TtxsEfjjUNZXD6T4E7xt9QMdwiPcs2FSTkWTqzFV8t/uw53eNAUolXtoGgStPLWRol+aOnW/O5DO4fHgXn21Xj+jm2Pnj4b5Z2rmnr596Dj8/w1Vu7xagUAFh/q2jbJfDGFcgePXrzby+yLff4K1vtlKyt2ZKif/HLfpuD0/P38QZD87zbKuoiiLhkFIqJtoclE5sRIGc7Jq4P6JnK/7+cfhU0Z2aN7D98Q/OXkv93Gye+WxTwL5/Lyrhw9U7uWVML2Z8UcyffjDQs+/r4j38efbagPdon4BSiadBIIMN79aCk7s2Z8GmPY7McXtpweaw+/cdqeCud1YCvk1XP3zyf0GPt9u8pZSKXdo2B2WW2O/gudl18yfgPxqpfpC8RBoElEo8DQIKqP3cPjl+QeBokGUsvWPA4fJKnTymVAJoEIhR+ybOLOCSLGr7mdudhyiccf3bAvDJt2X0v+sDZnxRnOBSKZV5NAjE6IvbRtd1EQLEcyMP1/Ky/O6z4zhzcFfNWBjxmDyrqerKZ12JaO+duZr561J/7QmlkokGgTRz0+ieUR1vp9m9ribP+Y8Oqqo2XP7MV1RaQ0e37TvK1TMWcri8MtjblVI2aBBIM7dYC9HXlWAdvLEq9VuTwK3SmhhxydNfMmf1Tj5YucOxz1Qq02gQUED8KSRO6NAYgHH923Dr+N5OFIlPvg3e9OMOAsW7XZPP/Fc8q6iq5vP1uxwpg1LpToNAGrA7aOYnpxUmrAzV1uReEYlqzkHLGFJv/+yFhRROmen5fdaKHUx5YxnV1Ya/zV3HPf9dyaX/WMDXxXvCnEUpBTpZLKPc9X/9Q+4L1jfw+/P62T5377aNWLX9QNT1iU7N67PrUPBmH4DdQfZ9vn63z+/vLHUtY73rUDlzrAVvAHaFaE5SStXQmkAacGJOlffTu7tdf2zfNrbe+8lvzuS0Hi29zmW/KjC0c7Ow+z+LolnHOwBA4P+XiqpqCqfM9KlFKJXpNAikASfmUHln/ezbrhHF0ybQuYW9vEFdWjSka8uGAJzYuWlU5YmUhO+mV5bYP5mfdTsP8eGqnew6VM7h8kqGTZ3j2bf38HG+2LCLpVv2hXz/F+t3UThlJpt2HQ55jFKpLmJzkIg8C5wHlBpjTrC2NQdeBQqBYuBHxpi91r7bgKuAKuBGY8wHCSm5CiB1mGZhaJdmfPzrMyls0YC/fbQegFG9W/Hx2tDj+gtbNKBFQR43ntWDR633OOnPH34LQJ+2jSgqbMZer1XMTrz3Q59jn7xsKKP7tuahD9Zy3Zk9aNIgl7eXuJqZvty42xPklEo3dmoCzwHj/bZNAeYaY3oCc63fEZF+wCSgv/Wex0XEuTGDypZvfj+WRyYN9qwFbIcTtYmuLRv6dAy3DTOr+vtDOjLvN6NcuYsSHLzW7DjIjv3h+wfeX7Gdd5dt46lPNzJt1hoAz1Khms1UpbOIQcAY8yngP8xiIjDDej0DON9r+yvGmHJjzCZgPTDMmaIqu5o1zGPi4A78+UeDKJ42odY/39205J5kds2Irnzz+7E+x9R23ro5q3eG3b/zwDFueXUpgGcymju9kXu9hh37j3HzK99wLEieI6VSVaz/FNsYY7YDWD/dq7J3ALZ4HVdibVMpJN5mJfeDc35uNmv/OJ7bz+1Ls4Z5Psf4ZAi13hDtbGcnfbmx5jknS8RnaUv363vfXcVbS7bx4arwAUWpVOL081iwu0fQurSIXCsiC0VkYVmZ5oOJR7I1VrjLI0C9nOygQSXLK4uo+/hwqaNrs7tDBLrd/h4vWusjPPWJa+GdA8cqar0sSiVarEFgp4i0A7B+usfmlQCdvI7rCGwLdgJjzHRjTJExpqhVq1YxFkM5xdFAYmPN42yvfRMGtgPgnAFtQx6/8b5zHSpcZLP9nvS37T9G8a7DzF/nGq4a7+xqpZJJrEHgHeBK6/WVwNte2yeJSD0R6Qr0BL6Kr4iqNkTq+1xzr//YgNA8ax6HuVd6LyfQp21jiqdNoFebRl7bGvkcX5sjn/YcPh6w7cyH5nmVpdaKolTCRQwCIvIy8D+gt4iUiMhVwDRgrIisA8Zav2OMWQm8BqwCZgHXG2O0F62WxHJvunF0Txrn5zC4U5Owx/nn54lXg3rBRydPOacPk8f2ommDwMylm+6vvdpAONe9uJhfvvxNXRdDKUdEnCdgjLk4xK6gCfWNMVOBqfEUSsUmliadkwqbs+zucby9ZKtnW7wPupGelG8a3ZOfn9E96D739gWbdgfsC1cbaFmQx65DgU/wifLfpdv428Un1trnKZUoOmNY2TbzxtOjOj5UE9MtY3tRPy98zeK+CwZEPP9Tlw/l9B4tefXa4VEHACdSXm/ZcyTuc2SiyqpqzzBcVfc0CKSRRDdV92/fhMb5kXMOOlGOLi0a8tAPB4Xc36R+LuP6t+VfV5/Myd1aBOz/+yVDwp7/01tHcce5feMq4+4gfQfxemNRCV9uDKwFpZNh982lyCuFh6pbGgRUVKLpoDVxjjmaMKBdyH1ZIYrRqXl9HrvkRM+Io1BaNaoXd/nO//vnbCw7FNN7jTE8MGsNi77bw2MfrfPMRfjVv5cyafqXPsdu33+UI8dTY/W0qmrDiq37A7bvPHCMkQ98TPGuw+w5fJx9Xik8VN3SVNLKeQ4Nnwk/ush35wc3j+Tr4j1cNryLZ9tFRZ14deEW/7c66qw/fwLAAz8YyGtfb+GPF5zA+L/O57ozu3Pr+D4h31d2sJzH523g8XmuOQgndm7mk4kV4FB5Jd9//AvW7jzI4E5Neev60xJ3IQ55Yt56Hpr9LW9dfxqDOzX1bH9k7jo27znCy19vrrvCqaC0JqACJNsQyGDl8d/Wu20jnwAAcNf3+jHppE7UhltfX8bC7/Yy/q/zAXh83gbWlx7ip899zdUzFgYc/5ZXRzzApf9YELCS2sLiPazdeRCAJWGynSaTVdsPAFCy17e/5CVr4l1jr/WqSw8eq72CqZA0CETpP9edyu8mxNeW7DSn85vdf2HkTlk74i2X+0YfLCbZaZZqkJfDtO8PDNiem+16b6Lzwo15+BM+WlPKnNU7fZa7rK423PfemoDjr3w29afUZGe5bilV1YYjxyt5acFmKrw6gbt5ZWO94O9fAPDArDW89nVgje3r4j3c//7qBJdYaRCI0omdm0XMgV9XnHiC/79B7enRulHkA8OVw/oZ7z3WPTM32A0/nkt954boRjn9+uxeTBzcPo5PdD3pHzxWwT8/38TWfUdtvSfZ0oHYUZN0z9Dvzg+4/T/LmfDofM9+r5RMnv8Pj8/bwK1vLANcfSWLvnPlcfrhk//jqU82xlyW619cHHKdalVD+wTSSG1nPL7ilC4JHSYZTZ+AHYM6NuGCEzvQt13jqN53w1k9OVxeyQntmzD1vdifTAfcPRuAe/67ytbxH/mtlGaMYdr7a3jq042M6NmSF6462dZ51pcepKBebtjU3k7xPAB4/S1+u7Om87wqzB/ptn1H+dkLi1i+db/P6C5jTNQzxo0xzFy+nZnLt0fMpFteWUW9nMzNeK81ARWz8wa2558/SVymcPc/+w5N6wfui+Ke0KFpfW4e05O3bzidH5/W1bPd/3a06HdjQp6jYb0crhnZjYEdw8+sdsqaHQd44cvvfLaVV1bz1KeuJ2N3HqPFm/dG7C8Y8/CnDL9/bkLK6c8dnF9fVBJ0v3d2Vn+nTvuI5dbIIu/Ji5Vh3hOK3Qeid5dto/fvZrHO6nvJRFoTiNOcyWdQFcMfaSIkW4duvHKys3j80iEMCbIOcTSX+vmUs4Ju979RNMiL/M/hretOY+6aUq55PrCz10k7DwQugnP0eGAGlgsfd7Wrh3rare2/TfcT+xcbgs918C/PL/61KOhx3odVVhmindsXbiGgpVv2MXXmap6/ahhzrGSBy7fup2eb+JpBU5XWBOLUo3UBvdtm5h9PKE4Go3MHtAvajJGIhHJZNv41ZGUJY/u1CXvMtAsHkBNqIoNNwTqJD/vNFdhrY7JasPQbiRTpa/FvDnp/xY4QR9YcdzyG2cXhQt+db6/gq+I9rNp+wKcjO1NpEFCOc7ev1stJ7j8v78liEwa2i6mfwd+Ht4xk0rDOrL/vXIqnTWDO5DN44tLws5ftuvwZ38DgvU7yVc99TeGUmXz6bZnPDa22016Hin0zfupqNjQ222m8Dxt0z2z+u3QbxhhKDxzjLx9+y97DxzleWc360oOc88h8XvObDxLuYxpayQsfmbPOM0zViSAw7f01FE6ZGfd5aps2BynH/eS0Qg6VV3DV6V0jH5wEhnRuyoM/GOizxkEkt47vzQOz1np+n3/rKLKzhPZ+/Rc9WhcEjJmP1aZdh0Pum7vG1Yl8hVWDuGZEV247py852TXXVDhlZsKXGw0VSN3pRuw+1PvX9H75smtZz9+87hpF9MjcdZzSrQX/s1Js3Pr6MiYObu95AAnXHORu9vMeObTN5oitcJ60Fh9KNcn9qKZsiZT+4IxerRjV25mFe+zcJ/Nzs/nNuD6Op5/2ZveJMhz3U/JJXZvTIC8nqmas687s4fN7p+YNAgKAm/vGOKJny4B9l57cmX/+5CT7H2zT0/M3MX/9LiqrQv9/2nUosN8hXqH+HxZYT9/hRgd5C7YGtfdcC8ATANzc/SMAX2zwPfZQeSVXPfc17y/fTsN6gX+Xj3603la57AjX+Z2MNAhkgBk/HRZxFI+78/XCE+0vCe3EjTiZeD992lnv+KnLh9o6rzsIVBvDTaN70rFZTbAQgdO6BwYHJ6zbeZBnPgs+zv6pTzZQ9Mc5vBFiFE/sgkcBz/oRMTQHuc2LMOZ/5bYDntcPfvCtz76pM1czd00pv3hxccgBAOWVzix9Eq4Wkow0CCjA9SRbPG0Co/q0ruui1LnBnZpybpjkdW7j+ruWw2zRMC/scf3au+YlXHV6V24Z24v5t47i5jE1QSY3OzHt9n+cuZo5fnMNNu8+wvKS/dz/vmvG8q/+vZS731np2GeGqgm4+4fs3h6DPUxHk3TO+wGlqtqw/2hNJ7p//4HbbW8ut33+cJ93rLKa45Wpkypb+wTSiK59G7/Zt4ykXZN827N637txBK0b1wt7TPOGeT5t8SJCc6/AUZtLZ4588OOAbc99UcwdE/pSXllNQb0cjlVUUV5ZTZP6gau7RRKqduiuDd35tr2AM2f1zsgHhfGt17j/GV8U+9QSQnUCL9i4J+bP8w5QA+/+gCb1c/nmzrNjPl9t0iCgonJWn9a8uXhr5AMTLFEV7pp1jl1BoHeEsePup/xU95N/fs1n63dxRq9Wng7TJy4dwjk2akTeKkL0QdT244n3fb70YDm7bSw6tHXfUXbsP+YzJHn/kQrKq6po3Sj8bGvvCW3VBvamUKpsbQ5KA52bNyAvO4vJZ/dK+GdNu3Bg0Bm8KS3Ifat5A9eT+qk9AhesceQjrc8MVntbP/Uc/vlj5zuLw/nM6nT1HjHzp1lr2H+kgg9X2X8qD7ViWF1OZKw2hkPl9tZjeOzjddz0yjeUHnANHT1p6hyGTY082/qwzfMnI60JpIEGeTl8O/WcWvmsvJwsOjStb7u5JJmFuzG1bpzP/FtH0S7B+XaClSEnO8unb+YHQzt60jC8dM3JlB4o5+ZXlyS0XADFu48w+uF57Dp0nCcvG0KP1gURkwtWhGhqqa0mr7P+PC9gVblgi9yE8q8vXSmv87KzePCHg2xPVAvWGbxlzxGOVVQl/UzkuGoCInKLiKwUkRUi8rKI5ItIcxH5UETWWT8D5/yrtFCXYyCcHIAR6lSdmjcgJ9hYxVrmvczmqd1b0qpR6D6ISJ3U0XKv3fzzfy1mzMOfRjy+rmsCG8sOc84j8322hUphEU6w+Q6LN+8NGVCCBYERD3zM2L98yv4jFWHneNS1mP/CRaQDcCNQZIw5AcgGJgFTgLnGmJ7AXOt3pZJOXXaj+3egzr91FDeM6sHt54Zejcwt3BDE+b8dFXfZ4rF5T/AaYqoNWcjKgnvf9c32euHjX3De3z4Leny4qQHf+/tnjHpoHuD63u9/b3VUtZNEi/cxJweoLyI5QANgGzARmGHtnwGcH+dnqDQxZ/IZPPvjorjOMbSLq2JZPy/+iWjJMJrbfXPs1LwBvx7Xm2tHdo/4HncMyAtSS6lvc4LeSYXOV9Arq6pZvf1A0H1OpOQIJVIup1iICM98tsnz+x6vPE0T//45r3zlu0xmuMD83W7XjPGt+45yvMqVCXbi3z93uMSxizkIGGO2Ag8Bm4HtwH5jzGygjTFmu3XMdkAHnqebGP8992hdwFl94vsHe9f/9QPs3+ySVS8r6eAgr3V47XLfT4PNFBcRsm0kr7v93L68dPXJMQXlfUeOs/OA79KQK7bup8cd7wc9vk3jegltDurRusDxc7qXw3Q7bdpHntdLt+xjit+cgu37Iy+V+dvXl3kCeDIlrIunOagZrqf+rkB7oKGIXBbF+68VkYUisrCsTFf/SSX3TjyBET1b+iwkXlvcT5RO/BM69wTX8McfDO3owNmic2r3lsy/dRQXDon+s90jirq3Cn7zczc15fkl8BverTkALQvyGNChCaf2aMlZfdrwwlXRrQkxbOpcTr7Pd8TMspLA5g13moxfntUz5eewHK0IP5v4un8tjngOp2YkOy2e0UFjgE3GmDIAEXkTOBXYKSLtjDHbRaQdUBrszcaY6cB0gKKiouQJiyqi3m0b2V7VymlOPlF2btEg4QnVwunUvEFM73P/P2jWIC/oSC33Q6Z/Qrwp5/TltjeX88YvTvHp8B7RM3xeqV5tCnxWBws2YqYgP/BW8uRlQ3l83np+VNQpoakU6iq8jH34E/q1b0yHpvUjBgmAr4v3JmVKiXj6BDYDw0WkgbjGf40GVgPvAFdax1wJvB1fEZVS3oI1B10+vAsvXzPc57hrRvquhT24U1Pev2lE2MVz+ljNVHnZWSz83Rg23X8uj0w60bM/1BoGwfonGtbL4Tfj+pCXk5XQ5qDc7CxuPKtH5AMdtq70EG8v2cbj8+xnD/WOActK9tmev5BI8fQJLABeBxYDy61zTQemAWNFZB0w1vpdKUelW/K6aLibVrybla8d2Y1Tursmtp07wJXT6JYxPaOu6bx1/WmAK8C0LKiHiPisyfzusm1B3xcp/1E8zUHNGoRPX5GXk8Xks3tTPG0Cs24eYfu8a+4d73mdqPxN/rxrAt977HNOuOuDgIyntS2u0UHGmLuMMX2MMScYYy43xpQbY3YbY0YbY3paP2NPyKGUn56tGzGyVyse+MHAui5KnfE8VZvgzWN/vehEvrpjdFQTtP57w+l88pszQz6x/9Ram/mh2TXZOQunzPQMdYzUzxnPQmuRchh5L17UJkJ6B2/eqc4Hdmwadbli8d7y7QHbLnl6Qa18dih1PxNGqSjk5WTx/E+H1do/2mRU0zke/M6bl5Plk+vmwiEdaFkQPsndgI5N6NKioeeJ3b+idd0o19DV8VbmVDf3uPmq6vAza+OZMfzoxTXNUeP7t+X6Ub7DaFsU1EyQi3Uoam0tgPTbN2LPVJooGgSUSjHu+5zdUYYP/2gwC383Jqpz+48sallQj5YF9cjye6Tv0LQ+H6zc4ck59OLVwQcMxBoCbhzd05PQLT83iycvH0qbxq7fbz+3D/d8rz8TB9WsgSER7mgjewV2grcsqGcrdbhbmwhZY1ON5g5SKsV4WoOM4bTuLXl14RbPurnxys3O4tdn92Jsv7ZB989b6zvYb9ehcn72wiLP76FyLcVaEcgWIddaDN5dO7n05C7Uz83mwiEdA+ZERKoJzPjJScxasYN6ua5zrvrDuKhrDyN7tuKmMT05/U+BabljMXFwe0fOEysNAkolqTmTR/rkwfcnItx7/gn8/MzuPusTxOuGs4KvqhZsOcpyv8VTcrKCP4qLCN1bNWRDWXQ5dHJzhOxs37kh2VnCD4s6BT0+Ut+DiPikxw43UiqUfy8q4UGvfE7x6tQstqHCTtHmIKWSVI/WjZg4OHC5T+9WoLycLLq2bFh7hYog3Dj4WMZz1c/N9qoJRD5DItNTJMpjHzu3vnEsNAgolaJq+3Z33ZmR8xplZwkf3jKSV68dHvKYeyf2p187e4vx1M/N9jT5JEumhed/Gt0M62SnQUCpFPDejSOYM/mMOi1DY6+hmvddMID+QVZV69isPj3bNOLkbkEW47Fu4qd0b8l7N4Uezz+mb2seu8Q1Imhw56aeMfyndk/MAj/RshOL3vjFKQkvh1M0CCiVAvq1b+xJlFZX8+S8P7dtk3pcdJJvu/zwbs3DDgX98WmFQM3omsIWodrChfMGtmfd1HPo07YxIsKcyWfw1OVDI5YxJ54JCTbZWUVsaJfmnlxNkXx4y8h4ixQXDQJKpZiBHZtwavcW/GHiCbX6uf/68jvP65ysLJ9JWgAFEUYoXXFKIcXTJtAo31WjiBTLcr1SUfRoXWCrEzcnO4s5k2O7qY7pG5jw2J26HODEzk35zbjejOsffORUrOp65TENAkqlmPzcbF66ZnitL3K/70hN3qCKqmqfGbcA9XKiS++dm6BV2zo3j62j3DtHktvzPx3GzWNco6VG9GjJ9aN62ErVbVek2dC1QYOAUsqW35/Xz/P68PGqgJpAtP5xRfC1DOId4BPrTdp/rsXIXq3imn/xg6HBh7F6u/TkzjGf3ykaBJRStlw4pKOnPb9HqwL8xyfdMaFvVOcr9BvaGqw5JhbBgkBOlkTdXzDSWg/B0xcSZXSqi3UqYqGTxZRStuTlZLHg9jEcPFZBo/xcine7Jn41ys9h6Z1nB6SUiFYih4B6ZwyNVerNQLBHawJKqai4O3bP6tOai4d1Zs7kM+IOAACVDkaBa73WUjh/cHtysrN8FtKJhlOlcqfpTjYaBJRSMcnPzeb+Cwd4ErrFq5vVPOTEENhWVtbUq0/vyl+DdPhGo5HVLxBp9FMkwZZjTYYJztocpJSqc3/6/gCaNcjjuS+KHTmfO812PDdZ95yHK08tBGrmObhlZ0lSLRgfK60JKKXqnNNj5Rt6nt7jH4KZl5PFNSO7BQxpvXx4l4Bjbx3fm3d/eXrcn1mbtCaglKozgzo1ZemWfY53ul5U1Imjx6u4/JTAG7VdkcoUrJZx3Zm1v9ZxvLQmoJSqOwnKgZGTncXVI7pFPYFtyZ1j+b9Brvz+ydBeXxviCgIi0lREXheRNSKyWkROEZHmIvKhiKyzfjaLfCalVCaqGYIvDOvanBYN8wKWj6xNTRvk0TzCwvZul57chSb1c2ndKLVXGou3JvAIMMsY0wcYBKwGpgBzjTE9gbnW70opFeD+Cwcwslcr+rZrRNMGeSz6/VhO7Jwaz409Whew9K6zade0ftTv/bHV2ZwMYg4CItIYGAk8A2CMOW6M2QdMBGZYh80Azo+viEqpdNW/fROe/+mwqJttEslTO7H9hshNWhcP800h4Z93qS7FUxPoBpQB/xSRb0TkHyLSEGhjjNkOYP10Zi64UkrVonBpsaN1/4UDfX7Pcy+ZmQQjTOMJAjnAEOAJY8yJwGGiaPoRkWtFZKGILCwrK4ujGEop5Zxob8zhDn/zulP55VmBI4YSlUE1FvGUpAQoMcYssH5/HVdQ2Cki7QCsn6XB3myMmW6MKTLGFLVq1SqOYiillHOinWiWF+aGPqRzM351du+A7U6k2XBKzEHAGLMD2CIi7iscDawC3gGutLZdCbwdVwmVUqoWuWsCdm/Tj14cX1qKuhbvZLFfAi+KSB6wEfgJrsDymohcBWwGfhjnZyilVNJqH8XooD99fwCvLSzx/J4EXQLxBQFjzBIg2MoQo+M5r1JK1RX3Ws4dm4VaAzl2F53UmYtO6szc1TsB6NuudleHC0ZMEnRPFxUVmYULF9Z1MZRSCmMMi77bS1GhvYXiAZ75bBOndm8R1U19fekhT8CJlYgsMsYEX6LNJs0dpJRSXkQkqgAAcNXpXaP+nHgDgFOSZ5ySUkqpWqdBQCmlMpgGAaWUymAaBJRSKoNpEFBKqQymQUAppTKYBgGllMpgGgSUUiqDJcWMYREpA75z6HQtgV0OnSsZ6PUkv3S7pnS7Hki/a3JfTxdjTFxpmJMiCDhJRBbGO406mej1JL90u6Z0ux5Iv2ty8nq0OUgppTKYBgGllMpg6RgEptd1ARym15P80u2a0u16IP2uybHrSbs+AaWUUvalY01AKaWUTSkXBESkWESWi8gSEVlobWsuIh+KyDrrZzOv428TkfUislZExtVdyT3leVZESkVkhde2qMsvIkOt/w/rReRREbvLYjsvxDXdLSJbre9piYic67Uvqa9JRDqJyMcislpEVorITdb2lPyewlxPKn9H+SLylYgsta7pHmt7qn5Hoa4n8d+RMSal/gOKgZZ+2x4AplivpwB/sl73A5YC9YCuwAYgu47LPxIYAqyIp/zAV8ApuNbDfh84J8mu6W7g10GOTfprAtoBQ6zXjYBvrXKn5PcU5npS+TsSoMB6nQssAIan8HcU6noS/h2lXE0ghInADOv1DOB8r+2vGGPKjTGbgPXAsNovXg1jzKfAHr/NUZVfRNoBjY0x/zOub/15r/fUuhDXFErSX5MxZrsxZrH1+iCwGuhAin5PYa4nlKS+HgDjcsj6Ndf6z5C631Go6wnFsetJxSBggNkiskhErrW2tTHGbAfXHzzQ2treAdji9d4Swv/x15Voy9/Beu2/PdncICLLrOYid7U8pa5JRAqBE3E9maX89+R3PZDC35GIZIvIEqAU+NAYk9LfUYjrgQR/R6kYBE4zxgwBzgGuF5GRYY4N1haWSsOhQpU/Fa7rCaA7MBjYDvzZ2p4y1yQiBcAbwM3GmAPhDg2yLemuKcj1pPR3ZIypMsYMBjriego+IczhSX9NIa4n4d9RygUBY8w262cp8B9czTs7rWoQ1s9S6/ASoJPX2zsC22qvtLZFW/4S67X/9qRhjNlp/VFXA09T0wyXEtckIrm4bpgvGmPetDan7PcU7HpS/TtyM8bsA+YB40nh78jN+3pq4ztKqSAgIg1FpJH7NXA2sAJ4B7jSOuxK4G3r9TvAJBGpJyJdgZ64Ok2STVTlt6q5B0VkuNXzf4XXe5KC+x+i5QJc3xOkwDVZn/8MsNoY87DXrpT8nkJdT4p/R61EpKn1uj4wBlhD6n5HQa+nVr6j2uj5duo/oBuuHvGlwErgDmt7C2AusM762dzrPXfg6jlfSx2OoPEqz8u4qnUVuKL2VbGUHyiy/iA2AI9hTfxLomt6AVgOLLP+YNulyjUBp+OqQi8Dllj/nZuq31OY60nl72gg8I1V9hXAndb2VP2OQl1Pwr8jnTGslFIZLKWag5RSSjlLg4BSSmUwDQJKKZXBNAgopVQG0yCglFIZTIOASnsiUmVlYFxpZWmcLCJZ1r4zRWS/V5bGJSIyxu99K0Tk3yLSweuYHX7ZHfNE5JDf5/5YRB6ri2tWyq6cui6AUrXgqHFNx0dEWgMvAU2Au6z9840x50V434vARV6/3w0cMsY85D44UsZepZKR1gRURjGudCPX4krKFc1dez7QIzGlUqruaE1AZRxjzEarOcidYXKElb3R7fvGmA3uX0QkB1fCwlkRTl3f7zzNcc3yVCppaRBQmcq7FhCqOcj7pj4fV/6dcDzNR+DqE8A1hV+ppKVBQGUcEekGVOHKMNk3zKE+N3Wl0pH2CaiMIiKtgCeBx4wmzlJKawIqI7ibdXKBSlyZGb1TRPv3CfzRGPN67RVPqbqjWUSVUiqDaXOQUkplMA0CSimVwTQIKKVUBtMgoJRSGUyDgFJKZTANAkoplcE0CCilVAbTIKCUUhns/wFSxmq7vfSNhwAAAABJRU5ErkJggg==\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": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='DEPTH'>"
      ]
     },
     "execution_count": 86,
     "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='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
}