08 - Curve Normalisation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gamma Ray Normalisation"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Created by:** Andy McDonald  \n",
    "\n",
    "This notebook illustrates carry out a simple normalisation on Gamma Ray data from the Volve Dataset.\n",
    "Medium Article Link: "
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What is Normalization?"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Normalization is the process of re-scaling or re-calibrating the well logs so that they are consistent with other logs in other wells within the field. This can be achieved by applying a single point normalization (linear shift) or a two point normalization ('stretch and squeeze') to the required curve. \n",
    "\n",
    "Normalization is commonly applied to gamma ray logs, but can be applied to neutron porosity, bulk density, sonic and spontaneous potential logs. Resistivity logs are generally not normalized unless there is a sufficient reason to do so (Shier, 2004). It should be noted that applying normalization can remove geological variations and features across the study area and should be considered carefully. Shier (2004) provides an excellent discussion and guidelines on how to carry out normalization on well log data."
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading and Checking Data\n",
    "The first step is to import the required libraries: pandas and matplotlib."
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('Data/VolveWells.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "         WELL  DEPTH  GR  AC  ACS  BS  CALI  DEN  NEU  PEF  RDEP  RMED\n0  15/9-F-1 C  145.9 NaN NaN  NaN NaN   NaN  NaN  NaN  NaN   NaN   NaN\n1  15/9-F-1 C  146.0 NaN NaN  NaN NaN   NaN  NaN  NaN  NaN   NaN   NaN\n2  15/9-F-1 C  146.1 NaN NaN  NaN NaN   NaN  NaN  NaN  NaN   NaN   NaN\n3  15/9-F-1 C  146.2 NaN NaN  NaN NaN   NaN  NaN  NaN  NaN   NaN   NaN\n4  15/9-F-1 C  146.3 NaN NaN  NaN NaN   NaN  NaN  NaN  NaN   NaN   NaN",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\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>WELL</th>\n      <th>DEPTH</th>\n      <th>GR</th>\n      <th>AC</th>\n      <th>ACS</th>\n      <th>BS</th>\n      <th>CALI</th>\n      <th>DEN</th>\n      <th>NEU</th>\n      <th>PEF</th>\n      <th>RDEP</th>\n      <th>RMED</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>15/9-F-1 C</td>\n      <td>145.9</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    </tr>\n    <tr>\n      <th>1</th>\n      <td>15/9-F-1 C</td>\n      <td>146.0</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    </tr>\n    <tr>\n      <th>2</th>\n      <td>15/9-F-1 C</td>\n      <td>146.1</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    </tr>\n    <tr>\n      <th>3</th>\n      <td>15/9-F-1 C</td>\n      <td>146.2</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    </tr>\n    <tr>\n      <th>4</th>\n      <td>15/9-F-1 C</td>\n      <td>146.3</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    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 3
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "array(['15/9-F-1 C', '15/9-F-4', '15/9-F-7'], dtype=object)"
     },
     "metadata": {},
     "execution_count": 4
    }
   ],
   "source": [
    "data['WELL'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the unique method on the dataframe, we can see that we have 3 wells within this Volve Data subset:  \n",
    "- 15/9-F-1 C\n",
    "- 15/9-F-4\n",
    "- 15/9-F-7"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting the Raw Data"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "             WELL     DEPTH  GR  AC  ACS    BS  CALI  DEN  NEU  PEF  RDEP  \\\n0      15/9-F-1 C  145.9000 NaN NaN  NaN   NaN   NaN  NaN  NaN  NaN   NaN   \n1      15/9-F-1 C  146.0000 NaN NaN  NaN   NaN   NaN  NaN  NaN  NaN   NaN   \n2      15/9-F-1 C  146.1000 NaN NaN  NaN   NaN   NaN  NaN  NaN  NaN   NaN   \n3      15/9-F-1 C  146.2000 NaN NaN  NaN   NaN   NaN  NaN  NaN  NaN   NaN   \n4      15/9-F-1 C  146.3000 NaN NaN  NaN   NaN   NaN  NaN  NaN  NaN   NaN   \n39481    15/9-F-4  200.3212 NaN NaN  NaN   NaN   NaN  NaN  NaN  NaN   NaN   \n39482    15/9-F-4  200.4736 NaN NaN  NaN   NaN   NaN  NaN  NaN  NaN   NaN   \n39483    15/9-F-4  200.6260 NaN NaN  NaN   NaN   NaN  NaN  NaN  NaN   NaN   \n39484    15/9-F-4  200.7784 NaN NaN  NaN   NaN   NaN  NaN  NaN  NaN   NaN   \n39485    15/9-F-4  200.9308 NaN NaN  NaN   NaN   NaN  NaN  NaN  NaN   NaN   \n61304    15/9-F-7  239.5700 NaN NaN  NaN   NaN   NaN  NaN  NaN  NaN   NaN   \n61305    15/9-F-7  239.7224 NaN NaN  NaN   NaN   NaN  NaN  NaN  NaN   NaN   \n61306    15/9-F-7  239.8748 NaN NaN  NaN   NaN   NaN  NaN  NaN  NaN   NaN   \n61307    15/9-F-7  240.0272 NaN NaN  NaN  17.5   NaN  NaN  NaN  NaN   NaN   \n61308    15/9-F-7  240.1796 NaN NaN  NaN  17.5   NaN  NaN  NaN  NaN   NaN   \n\n       RMED  \n0       NaN  \n1       NaN  \n2       NaN  \n3       NaN  \n4       NaN  \n39481   NaN  \n39482   NaN  \n39483   NaN  \n39484   NaN  \n39485   NaN  \n61304   NaN  \n61305   NaN  \n61306   NaN  \n61307   NaN  \n61308   NaN  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\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>WELL</th>\n      <th>DEPTH</th>\n      <th>GR</th>\n      <th>AC</th>\n      <th>ACS</th>\n      <th>BS</th>\n      <th>CALI</th>\n      <th>DEN</th>\n      <th>NEU</th>\n      <th>PEF</th>\n      <th>RDEP</th>\n      <th>RMED</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>15/9-F-1 C</td>\n      <td>145.9000</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    </tr>\n    <tr>\n      <th>1</th>\n      <td>15/9-F-1 C</td>\n      <td>146.0000</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    </tr>\n    <tr>\n      <th>2</th>\n      <td>15/9-F-1 C</td>\n      <td>146.1000</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    </tr>\n    <tr>\n      <th>3</th>\n      <td>15/9-F-1 C</td>\n      <td>146.2000</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    </tr>\n    <tr>\n      <th>4</th>\n      <td>15/9-F-1 C</td>\n      <td>146.3000</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    </tr>\n    <tr>\n      <th>39481</th>\n      <td>15/9-F-4</td>\n      <td>200.3212</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    </tr>\n    <tr>\n      <th>39482</th>\n      <td>15/9-F-4</td>\n      <td>200.4736</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    </tr>\n    <tr>\n      <th>39483</th>\n      <td>15/9-F-4</td>\n      <td>200.6260</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    </tr>\n    <tr>\n      <th>39484</th>\n      <td>15/9-F-4</td>\n      <td>200.7784</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    </tr>\n    <tr>\n      <th>39485</th>\n      <td>15/9-F-4</td>\n      <td>200.9308</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    </tr>\n    <tr>\n      <th>61304</th>\n      <td>15/9-F-7</td>\n      <td>239.5700</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    </tr>\n    <tr>\n      <th>61305</th>\n      <td>15/9-F-7</td>\n      <td>239.7224</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    </tr>\n    <tr>\n      <th>61306</th>\n      <td>15/9-F-7</td>\n      <td>239.8748</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    </tr>\n    <tr>\n      <th>61307</th>\n      <td>15/9-F-7</td>\n      <td>240.0272</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>17.5</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    </tr>\n    <tr>\n      <th>61308</th>\n      <td>15/9-F-7</td>\n      <td>240.1796</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>17.5</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    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 5
    }
   ],
   "source": [
    "wells = data.groupby('WELL')\n",
    "wells.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "               DEPTH        GR         AC         ACS     BS      CALI  \\\nWELL                                                                     \n15/9-F-1 C  145.9000  0.148800        NaN         NaN   8.50  8.304600   \n15/9-F-4    200.3212  2.738081  53.358017  108.222015   8.50  8.296204   \n15/9-F-7    239.5700  2.403074        NaN         NaN  12.25       NaN   \n\n                 DEN       NEU       PEF      RDEP      RMED  \nWELL                                                          \n15/9-F-1 C  1.750600  0.045900  4.226300  0.086700  0.064900  \n15/9-F-4    1.334123  0.045681  1.547774  0.480972  0.501000  \n15/9-F-7         NaN       NaN       NaN  0.665063  0.600974  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\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>DEPTH</th>\n      <th>GR</th>\n      <th>AC</th>\n      <th>ACS</th>\n      <th>BS</th>\n      <th>CALI</th>\n      <th>DEN</th>\n      <th>NEU</th>\n      <th>PEF</th>\n      <th>RDEP</th>\n      <th>RMED</th>\n    </tr>\n    <tr>\n      <th>WELL</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      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>15/9-F-1 C</th>\n      <td>145.9000</td>\n      <td>0.148800</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>8.50</td>\n      <td>8.304600</td>\n      <td>1.750600</td>\n      <td>0.045900</td>\n      <td>4.226300</td>\n      <td>0.086700</td>\n      <td>0.064900</td>\n    </tr>\n    <tr>\n      <th>15/9-F-4</th>\n      <td>200.3212</td>\n      <td>2.738081</td>\n      <td>53.358017</td>\n      <td>108.222015</td>\n      <td>8.50</td>\n      <td>8.296204</td>\n      <td>1.334123</td>\n      <td>0.045681</td>\n      <td>1.547774</td>\n      <td>0.480972</td>\n      <td>0.501000</td>\n    </tr>\n    <tr>\n      <th>15/9-F-7</th>\n      <td>239.5700</td>\n      <td>2.403074</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>12.25</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.665063</td>\n      <td>0.600974</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 6
    }
   ],
   "source": [
    "wells.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8,6))\n",
    "for label, df in wells:\n",
    "    df.GR.plot(kind ='kde', ax=ax, label=label)\n",
    "    plt.xlim(0, 200)\n",
    "plt.grid(True)\n",
    "plt.legend()\n",
    "plt.savefig('before_normalisation.png', dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the plot above, we will assume that the key well is 15/9-F-7 and we will normalise the other two datasets to this one.  \n",
    "  "
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculating the Percentiles"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is possible that datasets can contain erroneous values which may affect the minimum and the maximum values within a curve. Therefore, some interpreters prefer to base their normalisation parameters on percentiles.  \n",
    "  \n",
    "In this example, I have used the 5th and 95th percentiles.\n",
    "\n",
    "The first step is to calculate the percentile (or quantile as pandas refers to it) by grouping the data by wells and then applying the .quantile method to a specific column. In this case, GR. The quantile function takes in a decimal value, so a value of 0.05 is equivalent to the 5th percentile and 0.95 is equivalent to the 95th percentile.  "
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "WELL\n15/9-F-1 C     7.312130\n15/9-F-4      12.406060\n15/9-F-7      25.646473\nName: GR, dtype: float64\n"
    }
   ],
   "source": [
    "gr_percentile_05 = data.groupby('WELL')['GR'].quantile(0.05)\n",
    "print(gr_percentile_05)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This calculation generates a pandas Series object. We can see what is in the series by calling upon it like so."
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So now we need to bring that back into our main dataframe. We can do this using the map function, which will combine two data series that share a common column. Once it is mapped we can call upon the `.describe()` method and confirm that it has been added to the dataframe."
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['05_PERC'] = data['WELL'].map(gr_percentile_05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "              DEPTH            GR           AC          ACS            BS  \\\ncount  66843.000000  65154.000000  4815.000000  4243.000000  65745.000000   \nmean    1915.232292     59.063326    78.140015   142.814335     15.661887   \nstd     1108.591492     35.584211    13.415549    15.675952      5.936712   \nmin      145.900000      0.148800    53.358017   108.222015      8.500000   \n25%      910.546200     30.414851    67.192184   130.138168     12.250000   \n50%     1850.965600     60.246799    76.447159   142.093720     17.500000   \n75%     2859.950000     84.880524    87.095100   154.055313     17.500000   \nmax     4093.900000   1004.250000   130.757935   190.262665     36.000000   \n\n               CALI           DEN           NEU           PEF          RDEP  \\\ncount  15060.000000  14938.000000  15042.000000  14938.000000  48315.000000   \nmean       8.541083      2.401113      0.188021      5.433473      8.065491   \nstd        0.078575      0.154349      0.072923      1.507955    130.146455   \nmin        8.296204      1.334123      0.045681      1.547774      0.086700   \n25%        8.524083      2.256317      0.142900      4.832051      0.787265   \n50%        8.524300      2.417045      0.181000      5.357124      1.290400   \n75%        8.556900      2.539997      0.222500      6.267550      2.539700   \nmax        9.793900      3.201297      0.732381     20.646111   5571.435059   \n\n               RMED       05_PERC  \ncount  48338.000000  66843.000000  \nmean     176.557392     10.494494  \nstd     3304.415559      5.118566  \nmin        0.064900      7.312130  \n25%        0.792100      7.312130  \n50%        1.340900      7.312130  \n75%        2.848600     12.406060  \nmax    97543.421880     25.646473  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\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>DEPTH</th>\n      <th>GR</th>\n      <th>AC</th>\n      <th>ACS</th>\n      <th>BS</th>\n      <th>CALI</th>\n      <th>DEN</th>\n      <th>NEU</th>\n      <th>PEF</th>\n      <th>RDEP</th>\n      <th>RMED</th>\n      <th>05_PERC</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>66843.000000</td>\n      <td>65154.000000</td>\n      <td>4815.000000</td>\n      <td>4243.000000</td>\n      <td>65745.000000</td>\n      <td>15060.000000</td>\n      <td>14938.000000</td>\n      <td>15042.000000</td>\n      <td>14938.000000</td>\n      <td>48315.000000</td>\n      <td>48338.000000</td>\n      <td>66843.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>1915.232292</td>\n      <td>59.063326</td>\n      <td>78.140015</td>\n      <td>142.814335</td>\n      <td>15.661887</td>\n      <td>8.541083</td>\n      <td>2.401113</td>\n      <td>0.188021</td>\n      <td>5.433473</td>\n      <td>8.065491</td>\n      <td>176.557392</td>\n      <td>10.494494</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>1108.591492</td>\n      <td>35.584211</td>\n      <td>13.415549</td>\n      <td>15.675952</td>\n      <td>5.936712</td>\n      <td>0.078575</td>\n      <td>0.154349</td>\n      <td>0.072923</td>\n      <td>1.507955</td>\n      <td>130.146455</td>\n      <td>3304.415559</td>\n      <td>5.118566</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>145.900000</td>\n      <td>0.148800</td>\n      <td>53.358017</td>\n      <td>108.222015</td>\n      <td>8.500000</td>\n      <td>8.296204</td>\n      <td>1.334123</td>\n      <td>0.045681</td>\n      <td>1.547774</td>\n      <td>0.086700</td>\n      <td>0.064900</td>\n      <td>7.312130</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>910.546200</td>\n      <td>30.414851</td>\n      <td>67.192184</td>\n      <td>130.138168</td>\n      <td>12.250000</td>\n      <td>8.524083</td>\n      <td>2.256317</td>\n      <td>0.142900</td>\n      <td>4.832051</td>\n      <td>0.787265</td>\n      <td>0.792100</td>\n      <td>7.312130</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>1850.965600</td>\n      <td>60.246799</td>\n      <td>76.447159</td>\n      <td>142.093720</td>\n      <td>17.500000</td>\n      <td>8.524300</td>\n      <td>2.417045</td>\n      <td>0.181000</td>\n      <td>5.357124</td>\n      <td>1.290400</td>\n      <td>1.340900</td>\n      <td>7.312130</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>2859.950000</td>\n      <td>84.880524</td>\n      <td>87.095100</td>\n      <td>154.055313</td>\n      <td>17.500000</td>\n      <td>8.556900</td>\n      <td>2.539997</td>\n      <td>0.222500</td>\n      <td>6.267550</td>\n      <td>2.539700</td>\n      <td>2.848600</td>\n      <td>12.406060</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>4093.900000</td>\n      <td>1004.250000</td>\n      <td>130.757935</td>\n      <td>190.262665</td>\n      <td>36.000000</td>\n      <td>9.793900</td>\n      <td>3.201297</td>\n      <td>0.732381</td>\n      <td>20.646111</td>\n      <td>5571.435059</td>\n      <td>97543.421880</td>\n      <td>25.646473</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 10
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can then repeat the process for the 95th percentile:"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "gr_percentile_95 = data.groupby('WELL')['GR'].quantile(0.95)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "WELL\n15/9-F-1 C     98.774478\n15/9-F-4      118.843896\n15/9-F-7      110.541397\nName: GR, dtype: float64"
     },
     "metadata": {},
     "execution_count": 12
    }
   ],
   "source": [
    "gr_percentile_95"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['95_PERC'] = data['WELL'].map(gr_percentile_95)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "              DEPTH            GR           AC          ACS            BS  \\\ncount  66843.000000  65154.000000  4815.000000  4243.000000  65745.000000   \nmean    1915.232292     59.063326    78.140015   142.814335     15.661887   \nstd     1108.591492     35.584211    13.415549    15.675952      5.936712   \nmin      145.900000      0.148800    53.358017   108.222015      8.500000   \n25%      910.546200     30.414851    67.192184   130.138168     12.250000   \n50%     1850.965600     60.246799    76.447159   142.093720     17.500000   \n75%     2859.950000     84.880524    87.095100   154.055313     17.500000   \nmax     4093.900000   1004.250000   130.757935   190.262665     36.000000   \n\n               CALI           DEN           NEU           PEF          RDEP  \\\ncount  15060.000000  14938.000000  15042.000000  14938.000000  48315.000000   \nmean       8.541083      2.401113      0.188021      5.433473      8.065491   \nstd        0.078575      0.154349      0.072923      1.507955    130.146455   \nmin        8.296204      1.334123      0.045681      1.547774      0.086700   \n25%        8.524083      2.256317      0.142900      4.832051      0.787265   \n50%        8.524300      2.417045      0.181000      5.357124      1.290400   \n75%        8.556900      2.539997      0.222500      6.267550      2.539700   \nmax        9.793900      3.201297      0.732381     20.646111   5571.435059   \n\n               RMED       05_PERC       95_PERC  \ncount  48338.000000  66843.000000  66843.000000  \nmean     176.557392     10.494494    106.301846  \nstd     3304.415559      5.118566      9.290552  \nmin        0.064900      7.312130     98.774478  \n25%        0.792100      7.312130     98.774478  \n50%        1.340900      7.312130     98.774478  \n75%        2.848600     12.406060    118.843896  \nmax    97543.421880     25.646473    118.843896  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\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>DEPTH</th>\n      <th>GR</th>\n      <th>AC</th>\n      <th>ACS</th>\n      <th>BS</th>\n      <th>CALI</th>\n      <th>DEN</th>\n      <th>NEU</th>\n      <th>PEF</th>\n      <th>RDEP</th>\n      <th>RMED</th>\n      <th>05_PERC</th>\n      <th>95_PERC</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>66843.000000</td>\n      <td>65154.000000</td>\n      <td>4815.000000</td>\n      <td>4243.000000</td>\n      <td>65745.000000</td>\n      <td>15060.000000</td>\n      <td>14938.000000</td>\n      <td>15042.000000</td>\n      <td>14938.000000</td>\n      <td>48315.000000</td>\n      <td>48338.000000</td>\n      <td>66843.000000</td>\n      <td>66843.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>1915.232292</td>\n      <td>59.063326</td>\n      <td>78.140015</td>\n      <td>142.814335</td>\n      <td>15.661887</td>\n      <td>8.541083</td>\n      <td>2.401113</td>\n      <td>0.188021</td>\n      <td>5.433473</td>\n      <td>8.065491</td>\n      <td>176.557392</td>\n      <td>10.494494</td>\n      <td>106.301846</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>1108.591492</td>\n      <td>35.584211</td>\n      <td>13.415549</td>\n      <td>15.675952</td>\n      <td>5.936712</td>\n      <td>0.078575</td>\n      <td>0.154349</td>\n      <td>0.072923</td>\n      <td>1.507955</td>\n      <td>130.146455</td>\n      <td>3304.415559</td>\n      <td>5.118566</td>\n      <td>9.290552</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>145.900000</td>\n      <td>0.148800</td>\n      <td>53.358017</td>\n      <td>108.222015</td>\n      <td>8.500000</td>\n      <td>8.296204</td>\n      <td>1.334123</td>\n      <td>0.045681</td>\n      <td>1.547774</td>\n      <td>0.086700</td>\n      <td>0.064900</td>\n      <td>7.312130</td>\n      <td>98.774478</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>910.546200</td>\n      <td>30.414851</td>\n      <td>67.192184</td>\n      <td>130.138168</td>\n      <td>12.250000</td>\n      <td>8.524083</td>\n      <td>2.256317</td>\n      <td>0.142900</td>\n      <td>4.832051</td>\n      <td>0.787265</td>\n      <td>0.792100</td>\n      <td>7.312130</td>\n      <td>98.774478</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>1850.965600</td>\n      <td>60.246799</td>\n      <td>76.447159</td>\n      <td>142.093720</td>\n      <td>17.500000</td>\n      <td>8.524300</td>\n      <td>2.417045</td>\n      <td>0.181000</td>\n      <td>5.357124</td>\n      <td>1.290400</td>\n      <td>1.340900</td>\n      <td>7.312130</td>\n      <td>98.774478</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>2859.950000</td>\n      <td>84.880524</td>\n      <td>87.095100</td>\n      <td>154.055313</td>\n      <td>17.500000</td>\n      <td>8.556900</td>\n      <td>2.539997</td>\n      <td>0.222500</td>\n      <td>6.267550</td>\n      <td>2.539700</td>\n      <td>2.848600</td>\n      <td>12.406060</td>\n      <td>118.843896</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>4093.900000</td>\n      <td>1004.250000</td>\n      <td>130.757935</td>\n      <td>190.262665</td>\n      <td>36.000000</td>\n      <td>9.793900</td>\n      <td>3.201297</td>\n      <td>0.732381</td>\n      <td>20.646111</td>\n      <td>5571.435059</td>\n      <td>97543.421880</td>\n      <td>25.646473</td>\n      <td>118.843896</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 14
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create the Normalisation Function"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to normalize the data, we need create a custom function.  \n",
    "The following equation comes from Daniel Shier: 'Well Log Normalization: Methods and Guidelines'.\n",
    "\n",
    "$$Curve_{norm} = Ref_{low} +(Ref_{high}-Ref_{low}) * \\Bigg[ \\frac {CurveValue - Well_{low}}{ Well_{high} - Well_{low}}\\Bigg]$$"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def normalise(curve, ref_low, ref_high, well_low, well_high):\n",
    "    return ref_low + ((ref_high - ref_low) * ((curve - well_low) / (well_high - well_low)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now set of key well high and low parameters."
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "key_well_low = 25.6464\n",
    "key_well_high = 110.5413"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To apply the function to each value and use the correct percentiles for each well we can use the `apply()` method to the pandas dataframe and then a `lamda` function for our custom function."
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['GR_NORM'] = data.apply(lambda x: normalise(x['GR'], key_well_low, key_well_high, \n",
    "                                                 x['05_PERC'], x['95_PERC']), axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting the Normalized Data"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To view our final normalized data, we can re-use the code from above to generate the histogram. When we do, we can see that all curves have been normalized to our reference well."
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8,6))\n",
    "for label, df in wells:\n",
    "    df.GR_NORM.plot(kind ='kde', ax=ax, label=label)\n",
    "    plt.xlim(0, 200)\n",
    "plt.grid(True)\n",
    "plt.legend()\n",
    "plt.savefig('after_normalisation.png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.6.4 64-bit ('base': conda)",
   "language": "python",
   "name": "python36464bitbaseconda57932f4328ed418c8f3e34148e4838c8"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4-final"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}