GeostatsPy_variogram_calculation.ipynb

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    "## GeostatsPy: Variogram Calculation for Subsurface Data Analytics in Python \n",
    "\n",
    "\n",
    "### Michael Pyrcz, Associate Professor, University of Texas at Austin \n",
    "\n",
    "##### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n",
    "\n",
    "\n",
    "### PGE 383 Exercise: Basic Univariate Summary Statistics and Data Distribution Representativity Plotting in Python with GeostatsPy\n",
    "\n",
    "Here's a simple workflow with some basic variogram calcuation with irregularly sampled data. This should help you get started with variogram caculation in subsurface modeling.\n",
    "\n",
    "#### Spatial Continuity \n",
    "\n",
    "**Spatial Continuity** is the correlation between values over distance.\n",
    "\n",
    "* No spatial continuity – no correlation between values over distance, random values at each location in space regardless of separation distance.\n",
    "\n",
    "* Homogenous phenomenon have perfect spatial continuity, since all values as the same (or very similar) they are correlated. \n",
    "\n",
    "We need a statistic to quantify spatial continuity! A convenient method is the Semivariogram.\n",
    "\n",
    "#### The Semivariogram\n",
    "\n",
    "Function of difference over distance.\n",
    "\n",
    "* The expected (average) squared difference between values separated by a lag distance vector (distance and direction), $h$:\n",
    "\n",
    "\\begin{equation}\n",
    "\\gamma(\\bf{h}) = \\frac{1}{2 N(\\bf{h})} \\sum^{N(\\bf{h})}_{\\alpha=1} (z(\\bf{u}_\\alpha) - z(\\bf{u}_\\alpha + \\bf{h}))^2  \n",
    "\\end{equation}\n",
    "\n",
    "where $z(\\bf{u}_\\alpha)$ and $z(\\bf{u}_\\alpha + \\bf{h})$ are the spatial sample values at tail and head locations of the lag vector respectively.\n",
    "\n",
    "* Calculated over a suite of lag distances to obtain a continuous function.\n",
    "\n",
    "* the $\\frac{1}{2}$ term converts a variogram into a semivariogram, but in practice the term variogram is used instead of semivariogram.\n",
    "* We prefer the semivariogram because it relates directly to the covariance function, $C_x(\\bf{h})$ and univariate variance, $\\sigma^2_x$:\n",
    "\n",
    "\\begin{equation}\n",
    "C_x(\\bf{h}) = \\sigma^2_x - \\gamma(\\bf{h})\n",
    "\\end{equation}\n",
    "\n",
    "Note the correlogram is related to the covariance function as:\n",
    "\n",
    "\\begin{equation}\n",
    "\\rho_x(\\bf{h}) = \\frac{C_x(\\bf{h})}{\\sigma^2_x}\n",
    "\\end{equation}\n",
    "\n",
    "The correlogram provides of function of the $\\bf{h}-\\bf{h}$ scatter plot correlation vs. lag offset $\\bf{h}$.  \n",
    "\n",
    "\\begin{equation}\n",
    "-1.0 \\le \\rho_x(\\bf{h}) \\le 1.0\n",
    "\\end{equation}\n",
    "\n",
    "#### Variogram Observations\n",
    "\n",
    "The following are common observations for variograms that should assist with their practical use.\n",
    "\n",
    "##### Observation \\#1 - As distance increases, variability increase (in general).\n",
    "\n",
    "This is common since in general, over greater distance offsets, there is often more difference between the head and tail samples.\n",
    "\n",
    "In some cases, such as with spatial cyclicity of the hole effect variogram model the variogram may have negative slope over somelag distance intervals\n",
    "\n",
    "Negative slopes at lag distances greater than half the data extent are often caused by too few pairs for a reliable variogram calculation\n",
    "\n",
    "##### Observation \\#2 - Calculated with over all possible pairs separated by lag vector, $\\bf{𝐡}$.\n",
    "\n",
    "We scan through the entire data set, searching for all possible pair combinations with all other data.  We then calculate the variogram as one half the expectation of squared difference between all pairs.\n",
    "\n",
    "More pairs results in a more reliable measure.\n",
    "\n",
    "##### Observation \\#3 - Need to plot the sill to know the degree of correlation.\n",
    "\n",
    "**Sill** is the variance, $\\sigma^2_x$\n",
    "\n",
    "Given stationarity of the variance, $\\sigma^2_x$, and variogram $\\gamma(\\bf{h})$:\n",
    "\n",
    "we can define the covariance function:\n",
    "\n",
    "\\begin{equation}\n",
    "C_x(\\bf{h}) = \\sigma^2_x - \\gamma(\\bf{h})\n",
    "\\end{equation}\n",
    "\n",
    "The covariance measure is a measure of similarity over distance (the mirror image of the variogram as shown by the equation above).\n",
    "\n",
    "Given a standardized distribution $\\sigma^2_x = 1.0$, the covariance, $C_x(\\bf{h})$, is equal to the correlogram, $\\rho_x(\\bf{h})$: \n",
    "\n",
    "\\begin{equation}\n",
    "\\rho_x(\\bf{h}) = \\sigma^2_x - \\gamma(\\bf{h})\n",
    "\\end{equation}\n",
    "\n",
    "##### Observation \\#4 - The lag distance at which the variogram reaches the sill is know as the range.\n",
    "\n",
    "At the range, knowing the data value at the tail location provides no information about a value at the head location of the lag distance vector.\n",
    "\n",
    "##### Observation \\#5 - The nugget effect, a discontinuity at the origin\n",
    "\n",
    "Sometimes there is a discontinuity in the variogram at distances less than the minimum data spacing.  This is known as **nugget effect**.\n",
    "\n",
    "The ratio of nugget / sill, is known as relative nugget effect (%). Modeled as a discontinuity with no correlation structure that at lags, $h \\gt \\epsilon$, an infinitesimal lag distance, and perfect correlation at $\\bf{h} = 0$.\n",
    "Caution when including nuggect effect in the variogram model as measurement error, mixing populations cause apparent nugget effect\n",
    "\n",
    "This exercise demonstrates the semivariogram calculation with GeostatsPy. The steps include:\n",
    "\n",
    "1. generate a 2D model with sequential Gaussian simulation\n",
    "2. sample from the simulation\n",
    "3. calculate and visualize experimental semivariograms\n",
    "\n",
    "#### Objective \n",
    "\n",
    "In the PGE 383: Stochastic Subsurface Modeling class I want to provide hands-on experience with building subsurface modeling workflows. Python provides an excellent vehicle to accomplish this. I have coded a package called GeostatsPy with GSLIB: Geostatistical Library (Deutsch and Journel, 1998) functionality that provides basic building blocks for building subsurface modeling workflows. \n",
    "\n",
    "The objective is to remove the hurdles of subsurface modeling workflow construction by providing building blocks and sufficient examples. This is not a coding class per se, but we need the ability to 'script' workflows working with numerical methods.    \n",
    "\n",
    "#### Getting Started\n",
    "\n",
    "Here's the steps to get setup in Python with the GeostatsPy package:\n",
    "\n",
    "1. Install Anaconda 3 on your machine (https://www.anaconda.com/download/). \n",
    "2. From Anaconda Navigator (within Anaconda3 group), go to the environment tab, click on base (root) green arrow and open a terminal. \n",
    "3. In the terminal type: pip install geostatspy. \n",
    "4. Open Jupyter and in the top block get started by copy and pasting the code block below from this Jupyter Notebook to start using the geostatspy functionality. \n",
    "\n",
    "You will need to copy the data file to your working directory.  They are available here:\n",
    "\n",
    "* Tabular data - sample_data.csv at https://git.io/fh4gm.\n",
    "\n",
    "There are exampled below with these functions. You can go here to see a list of the available functions, https://git.io/fh4eX, other example workflows and source code. \n",
    "\n",
    "#### Load the required libraries\n",
    "\n",
    "The following code loads the required libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import geostatspy.GSLIB as GSLIB                       # GSLIB utilies, visualization and wrapper\n",
    "import geostatspy.geostats as geostats                 # GSLIB methods convert to Python    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will also need some standard packages. These should have been installed with Anaconda 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os                                               # to set current working directory \n",
    "import numpy as np                                      # arrays and matrix math\n",
    "import pandas as pd                                     # DataFrames\n",
    "import matplotlib.pyplot as plt                         # plotting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you get a package import error, you may have to first install some of these packages. This can usually be accomplished by opening up a command window on Windows and then typing 'python -m pip install [package-name]'. More assistance is available with the respective package docs.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "#### Set the working directory\n",
    "\n",
    "I always like to do this so I don't lose files and to simplify subsequent read and writes (avoid including the full address each time).  Also, in this case make sure to place the required (see above) GSLIB executables in this directory or a location identified in the environmental variable *Path*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir(\"c:/PGE383\")                                   # set the working directory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Loading Tabular Data\n",
    "\n",
    "Here's the command to load our comma delimited data file in to a Pandas' DataFrame object. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Facies</th>\n",
       "      <th>Porosity</th>\n",
       "      <th>Perm</th>\n",
       "      <th>AI</th>\n",
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       "       X      Y  Facies  Porosity       Perm           AI\n",
       "0  100.0  900.0     1.0  0.100187   1.363890  5110.699751\n",
       "1  100.0  800.0     0.0  0.107947  12.576845  4671.458560\n",
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       "3  100.0  600.0     0.0  0.108460   2.446678  5201.637996\n",
       "4  100.0  500.0     0.0  0.102468   1.952264  3835.270322"
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     "execution_count": 7,
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   ],
   "source": [
    "df = pd.read_csv(\"sample_data.csv\")                     # read a .csv file in as a DataFrame\n",
    "#print(df.iloc[0:5,:])                                  # display first 4 samples in the table as a preview\n",
    "df.head()                                               # we could also use this command for a table preview "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will work by-facies, that is separating sand and shale facies and working with them separately.  This command extracts the sand and shale 'Facies\" into new DataFrames for our analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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       "   index      X      Y  Facies  Porosity       Perm           AI\n",
       "0      0  100.0  900.0     1.0  0.100187   1.363890  5110.699751\n",
       "1      8  100.0  100.0     1.0  0.137453   5.727603  5823.241783\n",
       "2      9  200.0  900.0     1.0  0.137062  14.771314  5621.146994\n",
       "3     10  200.0  800.0     1.0  0.125984  10.675436  4292.700500\n",
       "4     18  300.0  900.0     1.0  0.111516  27.999817  4183.466773"
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   ],
   "source": [
    "# Separate Sample Data by-facies - Deepcopy\n",
    "df_sand = pd.DataFrame.copy(df[df['Facies'] == 1]).reset_index() # copy only 'Facies' = sand records\n",
    "df_shale = pd.DataFrame.copy(df[df['Facies'] == 0]).reset_index() # copy only 'Facies' = shale records\n",
    "df_sand.head()                                          # preview the sand only DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Summary Statistics for Tabular Data\n",
    "\n",
    "The table includes X and Y coordinates (meters), Facies 1 and 0 (1 is sandstone and 0 interbedded sand and mudstone), Porosity (fraction), and permeability as Perm (mDarcy). \n",
    "\n",
    "There are a lot of efficient methods to calculate summary statistics from tabular data in DataFrames. The 'describe' command provides count, mean, minimum, maximum, and quartiles all in a nice data table. We use transpose just to flip the table so that features are on the rows and the statistics are on the columns.\n",
    "\n",
    "Let's look at and compare the sumamry statistics for sand and shale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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       "      <td>162.0</td>\n",
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       "      <th>X</th>\n",
       "      <td>162.0</td>\n",
       "      <td>831.111111</td>\n",
       "      <td>238.857269</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>800.000000</td>\n",
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       "      <td>975.000000</td>\n",
       "      <td>1005.000000</td>\n",
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       "      <th>Y</th>\n",
       "      <td>162.0</td>\n",
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       "      <td>100.000000</td>\n",
       "      <td>469.000000</td>\n",
       "      <td>509.000000</td>\n",
       "      <td>549.000000</td>\n",
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       "    <tr>\n",
       "      <th>Facies</th>\n",
       "      <td>162.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Porosity</th>\n",
       "      <td>162.0</td>\n",
       "      <td>0.181001</td>\n",
       "      <td>0.037196</td>\n",
       "      <td>0.083842</td>\n",
       "      <td>0.155735</td>\n",
       "      <td>0.194823</td>\n",
       "      <td>0.207754</td>\n",
       "      <td>0.242298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Perm</th>\n",
       "      <td>162.0</td>\n",
       "      <td>293.798999</td>\n",
       "      <td>399.989890</td>\n",
       "      <td>0.381032</td>\n",
       "      <td>31.996547</td>\n",
       "      <td>155.888123</td>\n",
       "      <td>385.177730</td>\n",
       "      <td>2642.999829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AI</th>\n",
       "      <td>162.0</td>\n",
       "      <td>3441.701689</td>\n",
       "      <td>969.924695</td>\n",
       "      <td>1844.166880</td>\n",
       "      <td>2748.296631</td>\n",
       "      <td>3179.596509</td>\n",
       "      <td>3998.567914</td>\n",
       "      <td>6197.834381</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count         mean         std          min          25%  \\\n",
       "index     162.0   117.981481   59.058816     0.000000    75.250000   \n",
       "X         162.0   831.111111  238.857269    50.000000   800.000000   \n",
       "Y         162.0   526.987654  142.707797   100.000000   469.000000   \n",
       "Facies    162.0     1.000000    0.000000     1.000000     1.000000   \n",
       "Porosity  162.0     0.181001    0.037196     0.083842     0.155735   \n",
       "Perm      162.0   293.798999  399.989890     0.381032    31.996547   \n",
       "AI        162.0  3441.701689  969.924695  1844.166880  2748.296631   \n",
       "\n",
       "                  50%          75%          max  \n",
       "index      117.500000   157.750000   260.000000  \n",
       "X          945.000000   975.000000  1005.000000  \n",
       "Y          509.000000   549.000000   939.000000  \n",
       "Facies       1.000000     1.000000     1.000000  \n",
       "Porosity     0.194823     0.207754     0.242298  \n",
       "Perm       155.888123   385.177730  2642.999829  \n",
       "AI        3179.596509  3998.567914  6197.834381  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sand.describe().transpose()                          # summary table of sand only DataFrame statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>index</th>\n",
       "      <td>99.0</td>\n",
       "      <td>149.666667</td>\n",
       "      <td>93.588330</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>41.500000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>225.500000</td>\n",
       "      <td>259.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X</th>\n",
       "      <td>99.0</td>\n",
       "      <td>300.444444</td>\n",
       "      <td>196.365820</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>231.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>970.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y</th>\n",
       "      <td>99.0</td>\n",
       "      <td>425.111111</td>\n",
       "      <td>183.665784</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>376.000000</td>\n",
       "      <td>416.000000</td>\n",
       "      <td>456.000000</td>\n",
       "      <td>989.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Facies</th>\n",
       "      <td>99.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Porosity</th>\n",
       "      <td>99.0</td>\n",
       "      <td>0.100212</td>\n",
       "      <td>0.014483</td>\n",
       "      <td>0.058871</td>\n",
       "      <td>0.091902</td>\n",
       "      <td>0.101987</td>\n",
       "      <td>0.109846</td>\n",
       "      <td>0.141657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Perm</th>\n",
       "      <td>99.0</td>\n",
       "      <td>3.568463</td>\n",
       "      <td>6.782476</td>\n",
       "      <td>0.033611</td>\n",
       "      <td>0.723788</td>\n",
       "      <td>1.530878</td>\n",
       "      <td>3.743835</td>\n",
       "      <td>52.500870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AI</th>\n",
       "      <td>99.0</td>\n",
       "      <td>5450.493543</td>\n",
       "      <td>728.871517</td>\n",
       "      <td>3595.586977</td>\n",
       "      <td>4897.828718</td>\n",
       "      <td>5570.604405</td>\n",
       "      <td>5951.816775</td>\n",
       "      <td>7881.898531</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count         mean         std          min          25%  \\\n",
       "index      99.0   149.666667   93.588330     1.000000    41.500000   \n",
       "X          99.0   300.444444  196.365820    40.000000   201.000000   \n",
       "Y          99.0   425.111111  183.665784    29.000000   376.000000   \n",
       "Facies     99.0     0.000000    0.000000     0.000000     0.000000   \n",
       "Porosity   99.0     0.100212    0.014483     0.058871     0.091902   \n",
       "Perm       99.0     3.568463    6.782476     0.033611     0.723788   \n",
       "AI         99.0  5450.493543  728.871517  3595.586977  4897.828718   \n",
       "\n",
       "                  50%          75%          max  \n",
       "index      201.000000   225.500000   259.000000  \n",
       "X          231.000000   300.000000   970.000000  \n",
       "Y          416.000000   456.000000   989.000000  \n",
       "Facies       0.000000     0.000000     0.000000  \n",
       "Porosity     0.101987     0.109846     0.141657  \n",
       "Perm         1.530878     3.743835    52.500870  \n",
       "AI        5570.604405  5951.816775  7881.898531  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_shale.describe().transpose()                         # summary table of shale only DataFrame statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The facies have strong differences in their summary statistics.\n",
    "\n",
    "Let's transform the data grouped overall both facies (sand and shale) and separated by facies to normal score values (Gaussian distributed with a mean of 0.0 and variance of 1.0). This is required for sequential Gaussian simulation (common target for our variogram models) and the Gaussian transform assists with outliers and provides more interpretable variograms. \n",
    "\n",
    "Let's look at the inputs for the GeostatsPy nscore program.  Note the output include an ndarray with the transformed values (in the same order as the input data in Dataframe 'df' and column 'vcol'), and the transformation table in original values and also in normal score values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function geostatspy.geostats.nscore(df, vcol, wcol=0, ismooth=0, dfsmooth=0, smcol=0, smwcol=0)>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geostats.nscore                                         # see the input parameters required by the nscore function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following command will transform the Porosity and Permeabilty to standard normal. Note: we perform the transform for porsity and permeability and for each for the cases of:\n",
    "\n",
    "1. all facies together\n",
    "2. sand facies only\n",
    "3. shale facies only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Transform to Gaussian by Facies\n",
    "df['NPor'], tvPor, tnsPor = geostats.nscore(df, 'Porosity') # nscore transform for all facies porosity \n",
    "df_sand['NPor'], tvPorSand, tnsPorSand = geostats.nscore(df_sand, 'Porosity')  # nscore transform for sand facies porosity \n",
    "df_shale['NPor'], tvPorShale, tnsPorShale = geostats.nscore(df_shale, 'Porosity')  # nscore transform for shale facies porosity\n",
    "df['NPerm'], tvPermSand, tnsPermSand = geostats.nscore(df, 'Perm')  # nscore transform for all facies permeability\n",
    "df_sand['NPerm'], tvPermSand, tnsPermSand = geostats.nscore(df_sand, 'Perm')  # nscore transform for sand facies permeability\n",
    "df_shale['NPerm'], tvPermShale, tnsPermShale = geostats.nscore(df_shale, 'Perm')  # nscore transform for shale facies permeability"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at a previous of one of the DataFrames to make sure that we now have the normal score porosity and permeability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>index</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Facies</th>\n",
       "      <th>Porosity</th>\n",
       "      <th>Perm</th>\n",
       "      <th>AI</th>\n",
       "      <th>NPor</th>\n",
       "      <th>NPerm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.100187</td>\n",
       "      <td>1.363890</td>\n",
       "      <td>5110.699751</td>\n",
       "      <td>-2.158819</td>\n",
       "      <td>-2.158819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.137453</td>\n",
       "      <td>5.727603</td>\n",
       "      <td>5823.241783</td>\n",
       "      <td>-0.817609</td>\n",
       "      <td>-1.468475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>200.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.137062</td>\n",
       "      <td>14.771314</td>\n",
       "      <td>5621.146994</td>\n",
       "      <td>-0.839418</td>\n",
       "      <td>-0.955141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "      <td>200.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.125984</td>\n",
       "      <td>10.675436</td>\n",
       "      <td>4292.700500</td>\n",
       "      <td>-1.031153</td>\n",
       "      <td>-1.237102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18</td>\n",
       "      <td>300.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.111516</td>\n",
       "      <td>27.999817</td>\n",
       "      <td>4183.466773</td>\n",
       "      <td>-1.621370</td>\n",
       "      <td>-0.694044</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index      X      Y  Facies  Porosity       Perm           AI      NPor  \\\n",
       "0      0  100.0  900.0     1.0  0.100187   1.363890  5110.699751 -2.158819   \n",
       "1      8  100.0  100.0     1.0  0.137453   5.727603  5823.241783 -0.817609   \n",
       "2      9  200.0  900.0     1.0  0.137062  14.771314  5621.146994 -0.839418   \n",
       "3     10  200.0  800.0     1.0  0.125984  10.675436  4292.700500 -1.031153   \n",
       "4     18  300.0  900.0     1.0  0.111516  27.999817  4183.466773 -1.621370   \n",
       "\n",
       "      NPerm  \n",
       "0 -2.158819  \n",
       "1 -1.468475  \n",
       "2 -0.955141  \n",
       "3 -1.237102  \n",
       "4 -0.694044  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sand.head()                                          # preview sand DataFrame with nscore transforms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That looks good! One way to check is to see if the relative magnitudes of the normal score transformed values match the original values.  e.g. that the normal score transform of 0.10 porosity normal score is less than the normal score transform of 0.14 porsity.  Also, the normal score transform of values close to the mean value should be close to 0.0 \n",
    "\n",
    "Let's also check the original and transformed sand and shale porosity distributions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(121)                                        # plot original sand and shale porosity histograms\n",
    "plt.hist(df_sand['Porosity'], facecolor='red',bins=np.linspace(0.0,0.4,50),alpha=0.2,density=True,edgecolor='black',label='Sand')\n",
    "plt.hist(df_shale['Porosity'], facecolor='blue',bins=np.linspace(0.0,0.4,50),alpha=0.2,density=True,edgecolor='black',label = 'Shale')\n",
    "plt.xlim([0.05,0.25]); plt.ylim([0,35.0])\n",
    "plt.xlabel('Porosity (fraction)'); plt.ylabel('Frequency'); plt.title('Porosity Sand and Shale')\n",
    "plt.legend(loc='upper left')\n",
    "plt.grid(True)\n",
    "\n",
    "plt.subplot(122)                                        # plot nscore transformed sand and shale histograms\n",
    "plt.hist(df_sand['NPor'], facecolor='red',bins=np.linspace(-3.0,3.0,40),histtype=\"stepfilled\",alpha=0.2,density=True,cumulative=False,edgecolor='black',label='Sand')\n",
    "plt.hist(df_shale['NPor'], facecolor='blue',bins=np.linspace(-3.0,3.0,40),histtype=\"stepfilled\",alpha=0.2,density=True,cumulative=False,edgecolor='black',label='Shale')\n",
    "plt.xlim([-3.0,3.0]); plt.ylim([0,0.50])\n",
    "plt.xlabel('Nscore Porosity'); plt.ylabel('Density'); plt.title('Nscore Porosity Sand and Shale')\n",
    "plt.legend(loc='upper left')\n",
    "plt.grid(True)\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=1.2, wspace=0.2, hspace=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The normal score transform has correctly transformed the porosity over sand and shale facies to standard normal.  Let's plot the location maps of normal score transforms of porosity and permeability for all facies, sand facies and shale facies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cmap = plt.cm.plasma                    # color map\n",
    "plt.subplot(231)\n",
    "GSLIB.locmap_st(df,'X','Y','NPor',0,1000,0,1000,-3,3,'Nscore Porosity - All Facies','X (m)','Y (m)','Nscore Porosity',cmap)\n",
    "\n",
    "plt.subplot(232)\n",
    "GSLIB.locmap_st(df_sand,'X','Y','NPor',0,1000,0,1000,-3,3,'Nscore Porosity - Sand Facies','X (m)','Y (m)','Nscore Porosity',cmap)\n",
    "\n",
    "plt.subplot(233)\n",
    "GSLIB.locmap_st(df_shale,'X','Y','NPor',0,1000,0,1000,-3,3,'Nscore Porosity - Shale Facies','X (m)','Y (m)','Nscore Porosity',cmap)\n",
    "\n",
    "plt.subplot(234)\n",
    "GSLIB.locmap_st(df,'X','Y','NPerm',0,1000,0,1000,-3,3,'Nscore Permeability - All Facies','X (m)','Y (m)','Nscore Permeability',cmap)\n",
    "\n",
    "plt.subplot(235)\n",
    "GSLIB.locmap_st(df_sand,'X','Y','NPerm',0,1000,0,1000,-3,3,'Nscore Permeability - Sand Facies','X (m)','Y (m)','Nscore Permeability',cmap)\n",
    "\n",
    "plt.subplot(236)\n",
    "GSLIB.locmap_st(df_shale,'X','Y','NPerm',0,1000,0,1000,-3,3,'Nscore Permeability - Shale Facies','X (m)','Y (m)','Nscore Permeability',cmap)\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=1.8, wspace=0.4, hspace=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see what the parameters are for the gamv, irregular data, experimental variogram calculation program."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function geostatspy.geostats.gamv(df, xcol, ycol, vcol, tmin, tmax, xlag, xltol, nlag, azm, atol, bandwh, isill)>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geostats.gamv                                           # see the input parameters required by the gamv function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use the location maps to help determine good variogram calculation parameters.\n",
    "\n",
    "We are ready to calculate variogram! Let's calculate isotropic variograms for the transformed normal score porosity and permeability for sand, shale and mixed (without separating sand and shale).  Some information on the parameters that I chose:\n",
    "\n",
    "```p\n",
    "tmin = -9999.; tmax = 9999.; \n",
    "lag_dist = 100.0; lag_tol = 50.0; nlag = 7; bandh = 9999.9; azi = 0; atol = 90.0; isill = 1\n",
    "```\n",
    "* tmin, tmax are trimming limits - set to have no impact, no need to filter the data\n",
    "* lag_dist, lag_tol are the lag distance, lag tolerance - set based on the common data spacing (100m) and tolerance as 100% of lag distance for additonal smoothing\n",
    "* nlag is number of lags - set to extend just past 50 of the data extent\n",
    "* bandh is the horizontal band width - set to have no effect\n",
    "* azi is the azimuth -  it has not effect since we set atol, the azimuth tolerance, to 90.0\n",
    "* isill is a boolean to standardize the distribution to a variance of 1 - it has no effect since the nscore transform sets the variance to 1.0\n",
    "\n",
    "Let's try running these variograms and visualizing them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate Sample Data Isotropic Variograms\n",
    "tmin = -9999.; tmax = 9999.; \n",
    "lag_dist = 100.0; lag_tol = 100.0; nlag = 7; bandh = 9999.9; azi = 0; atol = 90.0; isill = 1\n",
    "\n",
    "lag, por_sand_gamma, por_sand_npair = geostats.gamv(df_sand,\"X\",\"Y\",\"NPor\",tmin,tmax,lag_dist,lag_tol,nlag,azi,atol,bandh,isill)\n",
    "lag, por_shale_gamma, por_shale_npair = geostats.gamv(df_shale,\"X\",\"Y\",\"NPor\",tmin,tmax,lag_dist,lag_tol,nlag,azi,atol,bandh,isill)\n",
    "lag, por_gamma, por_npair = geostats.gamv(df,\"X\",\"Y\",\"NPor\",tmin,tmax,lag_dist,lag_tol,nlag,azi,atol,bandh,isill)\n",
    "\n",
    "lag, perm_sand_gamma, perm_sand_npair = geostats.gamv(df_sand,\"X\",\"Y\",\"NPerm\",tmin,tmax,lag_dist,lag_tol,nlag,azi,atol,bandh,isill)\n",
    "lag, perm_shale_gamma, perm_shale_npair = geostats.gamv(df_shale,\"X\",\"Y\",\"NPerm\",tmin,tmax,lag_dist,lag_tol,nlag,azi,atol,bandh,isill)\n",
    "lag, perm_gamma, perm_npair = geostats.gamv(df,\"X\",\"Y\",\"NPerm\",tmin,tmax,lag_dist,lag_tol,nlag,azi,atol,bandh,isill)\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.plot(lag,por_gamma,'x',color = 'black',label = 'All')\n",
    "plt.plot(lag,por_sand_gamma,'x',color = 'orange',label = 'Sand')\n",
    "plt.plot(lag,por_shale_gamma,'x',color = 'brown',label = 'Shale')\n",
    "plt.plot([0,2000],[1.0,1.0],color = 'black')\n",
    "plt.xlabel(r'Lag Distance $\\bf(h)$, (m)')\n",
    "plt.ylabel(r'$\\gamma \\bf(h)$')\n",
    "plt.title('Isotropic NSCORE Porosity Variogram')\n",
    "plt.xlim([0,700])\n",
    "plt.ylim([0,1.8])\n",
    "plt.legend(loc='upper left')\n",
    "plt.grid(True)\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.plot(lag,perm_gamma,'x',color = 'black',label = 'All')\n",
    "plt.plot(lag,perm_sand_gamma,'x',color = 'orange',label = 'Sand')\n",
    "plt.plot(lag,perm_shale_gamma,'x',color = 'brown',label = 'Shale')\n",
    "plt.plot([0,2000],[1.0,1.0],color = 'black')\n",
    "plt.xlabel(r'Lag Distance $\\bf(h)$, (m)')\n",
    "plt.ylabel(r'$\\gamma \\bf(h)$')\n",
    "plt.title('Isotropic NSCORE Permeaiblity Variogram')\n",
    "plt.xlim([0,700])\n",
    "plt.ylim([0,1.8])\n",
    "plt.legend(loc='upper left')\n",
    "plt.grid(True)\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=1.2, wspace=0.2, hspace=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The experiental variograms have some interesting features:\n",
    "\n",
    "* the range of the sand porosity and permeability is greater than the shale porosity and permeability range\n",
    "* although the shale short range experimental points may be nosy due to sparce shale data\n",
    "\n",
    "There is much more that you could try out, e.g. direction variograms."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I hope you find this code and demonstration useful. I'm always happy to discuss geostatistics, statistical modeling, uncertainty modeling and machine learning,\n",
    "\n",
    "*Michael*\n",
    "\n",
    "**Michael Pyrcz**, Ph.D., P.Eng. Associate Professor The Hildebrand Department of Petroleum and Geosystems Engineering, Bureau of Economic Geology, The Jackson School of Geosciences, The University of Texas at Austin\n",
    "On Twitter I'm the **GeostatsGuy** and on YouTube my lectures are on the channel, **GeostatsGuy Lectures**."
   ]
  }
 ],
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