5stepsV2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5 Steps in Pandas to Process Petrophysical Well Logs(part2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This project is the second part of the previous work that can be found [here](https://towardsdatascience.com/10-steps-in-pandas-to-process-las-file-and-plot-610732093338). To start, we will use the output of previous work to eliminate extra work as already we have processed. If you have worked on a previous project you may write DataFrame into csv file (df.to_csv(\"1050383876v2.csv\")) to use here. Otherwise, you can access from [my github account](https://github.com/mardani72/LAS_Pandas) in the csv format called 1050383876v2.csv.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this work, I will cover the following steps:<br>\n",
    "1) Function Definition<br>\n",
    "2) Apply Function<br>\n",
    "3) Lambda Function<br>\n",
    "4) Cut Function<br>\n",
    "5) Visualization<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let's read the file into pandas dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DEPT</th>\n",
       "      <th>CNPOR</th>\n",
       "      <th>GR</th>\n",
       "      <th>RHOB</th>\n",
       "      <th>DT</th>\n",
       "      <th>MELCAL</th>\n",
       "      <th>SPOR</th>\n",
       "      <th>Vsh</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>4.62</td>\n",
       "      <td>10.82</td>\n",
       "      <td>2.93</td>\n",
       "      <td>51.77</td>\n",
       "      <td>8.26</td>\n",
       "      <td>2.95</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000.5</td>\n",
       "      <td>4.29</td>\n",
       "      <td>11.34</td>\n",
       "      <td>2.94</td>\n",
       "      <td>51.98</td>\n",
       "      <td>8.22</td>\n",
       "      <td>3.10</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1001.0</td>\n",
       "      <td>4.14</td>\n",
       "      <td>11.72</td>\n",
       "      <td>2.93</td>\n",
       "      <td>52.10</td>\n",
       "      <td>8.20</td>\n",
       "      <td>3.18</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1001.5</td>\n",
       "      <td>4.03</td>\n",
       "      <td>13.02</td>\n",
       "      <td>2.91</td>\n",
       "      <td>52.13</td>\n",
       "      <td>8.20</td>\n",
       "      <td>3.20</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1002.0</td>\n",
       "      <td>3.94</td>\n",
       "      <td>12.72</td>\n",
       "      <td>2.88</td>\n",
       "      <td>52.07</td>\n",
       "      <td>8.19</td>\n",
       "      <td>3.16</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     DEPT  CNPOR     GR  RHOB     DT  MELCAL  SPOR   Vsh\n",
       "0  1000.0   4.62  10.82  2.93  51.77    8.26  2.95  0.02\n",
       "1  1000.5   4.29  11.34  2.94  51.98    8.22  3.10  0.02\n",
       "2  1001.0   4.14  11.72  2.93  52.10    8.20  3.18  0.02\n",
       "3  1001.5   4.03  13.02  2.91  52.13    8.20  3.20  0.02\n",
       "4  1002.0   3.94  12.72  2.88  52.07    8.19  3.16  0.02"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('1050383876v2.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1- Function defintion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Function in python is a group of statements that perform a specific task. A function can make the larger program more readable. It also can help us to prevent repetition. A function has special structure as:\n",
    "1- Keyword def, 2- function Name, 3-arguments that we pass values to a function, 4-A colon(:), 5-more statement in body, 6-Retrun statement which is optional."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Porosity calculation from Density log"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are several approaches to calculate porosity from petrophysical well logs. Density log measures rock medium bulk density in wellbore condition. Having knowledge of fluid's density filling rock porosity and grain density can help us to calculate the percentage of void areas of the rocks. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def den(rb, rf, rm):\n",
    "    # rb = bulk density from well log readings\n",
    "    # rf = fluid density\n",
    "    # rm = matrix density or grain density of rocks\n",
    "    return (rm-rb)*100/(rm-rf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2- Apply Function "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the apply function, a predefined function (here, density porosity) can be applied to each elements of DataFrame either in column or row direction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DEPT</th>\n",
       "      <th>CNPOR</th>\n",
       "      <th>GR</th>\n",
       "      <th>RHOB</th>\n",
       "      <th>DT</th>\n",
       "      <th>MELCAL</th>\n",
       "      <th>SPOR</th>\n",
       "      <th>Vsh</th>\n",
       "      <th>DNPOR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>4.62</td>\n",
       "      <td>10.82</td>\n",
       "      <td>2.93</td>\n",
       "      <td>51.77</td>\n",
       "      <td>8.26</td>\n",
       "      <td>2.95</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-12.865497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000.5</td>\n",
       "      <td>4.29</td>\n",
       "      <td>11.34</td>\n",
       "      <td>2.94</td>\n",
       "      <td>51.98</td>\n",
       "      <td>8.22</td>\n",
       "      <td>3.10</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-13.450292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1001.0</td>\n",
       "      <td>4.14</td>\n",
       "      <td>11.72</td>\n",
       "      <td>2.93</td>\n",
       "      <td>52.10</td>\n",
       "      <td>8.20</td>\n",
       "      <td>3.18</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-12.865497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1001.5</td>\n",
       "      <td>4.03</td>\n",
       "      <td>13.02</td>\n",
       "      <td>2.91</td>\n",
       "      <td>52.13</td>\n",
       "      <td>8.20</td>\n",
       "      <td>3.20</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-11.695906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1002.0</td>\n",
       "      <td>3.94</td>\n",
       "      <td>12.72</td>\n",
       "      <td>2.88</td>\n",
       "      <td>52.07</td>\n",
       "      <td>8.19</td>\n",
       "      <td>3.16</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-9.941520</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     DEPT  CNPOR     GR  RHOB     DT  MELCAL  SPOR   Vsh      DNPOR\n",
       "0  1000.0   4.62  10.82  2.93  51.77    8.26  2.95  0.02 -12.865497\n",
       "1  1000.5   4.29  11.34  2.94  51.98    8.22  3.10  0.02 -13.450292\n",
       "2  1001.0   4.14  11.72  2.93  52.10    8.20  3.18  0.02 -12.865497\n",
       "3  1001.5   4.03  13.02  2.91  52.13    8.20  3.20  0.02 -11.695906\n",
       "4  1002.0   3.94  12.72  2.88  52.07    8.19  3.16  0.02  -9.941520"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"DNPOR\"]=df['RHOB'].apply(den, rf=1, rm=2.71 )\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As den function takes 3 values (rb, rf, rm), we can introduce the main one from the dataset (rb =df['RHOB']) and use the apply function to define rf and rm constants as manual inputs. Here I assume that fluid content is water with density of 1 and the dominant mineral is Calcite with a density of 2.71. The Density porosity is stored in new column called DNPOR."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3- Lambda Function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lambda function is a simple 1-line function that does not have def or return keywords. In fact, they are implicit. To use this function, we need to type lambda followed by parameters. Then, the colon comes before the return argument."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Total Porosity Calculation from Density and Neutron porosity "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "Tot_por = lambda DN,CN: (DN+CN)/2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Total porosity is the average of density and neutron porosity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The function name is Tot_por. For lambda function, DN and CN are density and neutron parameters followed by a colon. The average of these two inputs will be returned by the function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DEPT</th>\n",
       "      <th>CNPOR</th>\n",
       "      <th>GR</th>\n",
       "      <th>RHOB</th>\n",
       "      <th>DT</th>\n",
       "      <th>MELCAL</th>\n",
       "      <th>SPOR</th>\n",
       "      <th>Vsh</th>\n",
       "      <th>DNPOR</th>\n",
       "      <th>TPOR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>4.62</td>\n",
       "      <td>10.82</td>\n",
       "      <td>2.93</td>\n",
       "      <td>51.77</td>\n",
       "      <td>8.26</td>\n",
       "      <td>2.95</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-13.529412</td>\n",
       "      <td>-4.454706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000.5</td>\n",
       "      <td>4.29</td>\n",
       "      <td>11.34</td>\n",
       "      <td>2.94</td>\n",
       "      <td>51.98</td>\n",
       "      <td>8.22</td>\n",
       "      <td>3.10</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-14.117647</td>\n",
       "      <td>-4.913824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1001.0</td>\n",
       "      <td>4.14</td>\n",
       "      <td>11.72</td>\n",
       "      <td>2.93</td>\n",
       "      <td>52.10</td>\n",
       "      <td>8.20</td>\n",
       "      <td>3.18</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-13.529412</td>\n",
       "      <td>-4.694706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1001.5</td>\n",
       "      <td>4.03</td>\n",
       "      <td>13.02</td>\n",
       "      <td>2.91</td>\n",
       "      <td>52.13</td>\n",
       "      <td>8.20</td>\n",
       "      <td>3.20</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-12.352941</td>\n",
       "      <td>-4.161471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1002.0</td>\n",
       "      <td>3.94</td>\n",
       "      <td>12.72</td>\n",
       "      <td>2.88</td>\n",
       "      <td>52.07</td>\n",
       "      <td>8.19</td>\n",
       "      <td>3.16</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-10.588235</td>\n",
       "      <td>-3.324118</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     DEPT  CNPOR     GR  RHOB     DT  MELCAL  SPOR   Vsh      DNPOR      TPOR\n",
       "0  1000.0   4.62  10.82  2.93  51.77    8.26  2.95  0.02 -13.529412 -4.454706\n",
       "1  1000.5   4.29  11.34  2.94  51.98    8.22  3.10  0.02 -14.117647 -4.913824\n",
       "2  1001.0   4.14  11.72  2.93  52.10    8.20  3.18  0.02 -13.529412 -4.694706\n",
       "3  1001.5   4.03  13.02  2.91  52.13    8.20  3.20  0.02 -12.352941 -4.161471\n",
       "4  1002.0   3.94  12.72  2.88  52.07    8.19  3.16  0.02 -10.588235 -3.324118"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['TPOR'] = Tot_por(df['CNPOR'], df[\"DNPOR\"])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calling the Tot_por function and introducing corresponding columns of DataFrame as input will create total porosity that will be stored in new column called TPOR in the dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4- Cut Function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When we need to segment and sort data values into specific bins, we can use Cut function. Here we will use this function to define simple rock facies based on petrophysical properties."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Facies categorization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['facies_gr'] = pd.cut(df['GR'], bins=[0,40,300], labels=['clean', 'shaly'] )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Facies classification is a huge topic in geoscience and various metrics can come to play but here, we look at it very simple. Based on GR reading in well logs, we can identify clean from shaly formations. A new column is added to the dataset based on GR readings which are binned between 0 and40(clean), and between 40and 300(shaly)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5- Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mainly, the matplotlib library is common in python for plotting. In this work, I prefer to use seaborn library for its simplicity. I want to visualize a scatter plot of density porosity vs. neutron porosity with the legend color of facies that in previous part we defined. This is simply accessible by one line of code while in matplotlib it requires for loop."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 423x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ab = sns.lmplot(x='DNPOR', y='SPOR', hue='facies_gr', data=df)\n",
    "plt.title('Sonic Porosity vs. Density Porosity', pad=-10)\n",
    "plt.savefig(fname='ABD.png',dpi = 300)"
   ]
  },
  {
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}