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"colab": {
"name": "BoundaryModeling.ipynb",
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"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
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"source": [
"![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\")
"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Es7hFF5hgJyv",
"colab_type": "text"
},
"source": [
"# Boundary Modeling\n",
"\n",
"The following notebook is comprised of 7 primary steps:\n",
"1. Initialize required packages, directories and parameters\n",
"2. Load and inspect the domain indicator data\n",
"3. Calculate and model the boundary indicator variogram\n",
"4. Calculate and model the Gaussian variogram that yields the indicator variogram when truncated\n",
"5. Model the distance function\n",
"6. Simulate boundary realizations, through truncation of simulated distance function deviates\n",
"7. Save project setting and clean the output files"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "s8mFU5OBgJyw",
"colab_type": "text"
},
"source": [
"## 1. Initialize required packages and parameters"
]
},
{
"cell_type": "code",
"metadata": {
"ExecuteTime": {
"end_time": "2020-06-03T02:26:04.994131Z",
"start_time": "2020-06-03T02:26:02.400131Z"
},
"id": "pwMM_0UUgJyx",
"colab_type": "code",
"colab": {},
"outputId": "b2359b9f-0ce0-4869-e18a-cce7286ad46f"
},
"source": [
"import pygeostat as gs\n",
"import numpy as np\n",
"import os\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Loading default Pygeostat Parameters from C:\\Users\\MostafaHadavand\\.Pygeostat\\Parameters.json\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Rh4DF_f5gJy0",
"colab_type": "text"
},
"source": [
"### Project settings\n",
"Load the previously set Matplotlib and Pygeostat settings."
]
},
{
"cell_type": "code",
"metadata": {
"ExecuteTime": {
"end_time": "2020-06-03T02:26:05.009133Z",
"start_time": "2020-06-03T02:26:04.996130Z"
},
"id": "iAliMytmgJy1",
"colab_type": "code",
"colab": {}
},
"source": [
"#path to GSLIB executables\n",
"exe_dir=\"../pygeostat/executable/\"\n",
"\n",
"gs.Parameters['data.griddef'] = gs.GridDef('''\n",
"120 5.0 10.0 \n",
"110 1205.0 10.0 \n",
"1 0.5 1.0''')\n",
"\n",
"gs.Parameters['data.catdict'] = {1: 'Inside', 0: 'Outside'}\n",
"\n",
"# Data values\n",
"gs.Parameters['data.tmin'] = -998\n",
"gs.Parameters['data.null'] = -999\n",
"\n",
"# Color map settings\n",
"gs.Parameters['plotting.cmap'] = 'bwr'\n",
"gs.Parameters['plotting.cmap_cat'] = 'bwr'\n",
"\n",
"# Number of realizations\n",
"nreal = 100\n",
"gs.Parameters['data.nreal'] = nreal\n",
"\n",
"# Parallel Processing threads\n",
"gs.Parameters['config.nprocess'] = 4\n",
"\n",
"# Pot Style settings\n",
"gs.PlotStyle['legend.fontsize'] = 12\n",
"gs.PlotStyle['font.size'] = 11"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "Izaoez2LgJy4",
"colab_type": "text"
},
"source": [
"### Directories"
]
},
{
"cell_type": "code",
"metadata": {
"ExecuteTime": {
"end_time": "2020-06-03T02:26:05.018132Z",
"start_time": "2020-06-03T02:26:05.013133Z"
},
"id": "kKWzaP3SgJy5",
"colab_type": "code",
"colab": {}
},
"source": [
"# Create the output directory\n",
"outdir = 'Output/'\n",
"gs.mkdir(outdir)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "B0UA7GcGgJy8",
"colab_type": "text"
},
"source": [
"## 2. Load and Inspect the Boundary Data\n",
"\n",
"Note that content in this section was explained in the introduction notebooks. Only new concepts are generally annotated in detail.\n",
"\n",
"### Load the data and note its attributes"
]
},
{
"cell_type": "code",
"metadata": {
"ExecuteTime": {
"end_time": "2020-06-03T02:26:05.094132Z",
"start_time": "2020-06-03T02:26:05.025161Z"
},
"id": "mXJ-pM6dgJy8",
"colab_type": "code",
"colab": {},
"outputId": "693734f2-c896-4c77-cbdb-e07ef6228940"
},
"source": [
"dat = gs.ExampleData('reservoir_boundary', cat='Domain Indicator')\n",
"dat.info"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"\"DataFile: c:\\\\users\\\\mostafahadavand\\\\anaconda3\\\\envs\\\\pygeostat_public\\\\lib\\\\site-packages\\\\pygeostat\\\\data\\\\example_data\\\\reservoir_boundary.dat\\nAttributes:\\ndh: 'HoleID', x: 'X', y: 'Y', cat: 'Domain Indicator', \\n\""
]
},
"metadata": {
"tags": []
},
"execution_count": 4
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "13wk2zFsgJzA",
"colab_type": "text"
},
"source": [
"### Data content and summary statistics"
]
},
{
"cell_type": "code",
"metadata": {
"ExecuteTime": {
"end_time": "2020-06-03T02:26:05.148132Z",
"start_time": "2020-06-03T02:26:05.097132Z"
},
"id": "AyMDhyEagJzA",
"colab_type": "code",
"colab": {},
"outputId": "357cfd2b-c474-43a7-c21b-ec1e0d4049ad"
},
"source": [
"print(dat.describe())\n",
"dat.head()"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
" HoleID X Y Domain Indicator\n",
"count 301.000000 301.000000 301.000000 301.000000\n",
"mean 151.000000 585.313156 1743.896678 0.764120\n",
"std 87.035433 326.620938 290.727190 0.425255\n",
"min 1.000000 5.370000 1205.440000 0.000000\n",
"25% 76.000000 325.710000 1495.580000 1.000000\n",
"50% 151.000000 605.210000 1735.870000 1.000000\n",
"75% 226.000000 835.830000 1985.890000 1.000000\n",
"max 301.000000 1185.420000 2285.760000 1.000000\n"
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"cell_type": "markdown",
"metadata": {
"id": "IW8EcXu3gJzG",
"colab_type": "text"
},
"source": [
"## 3. Calculate and Model the Indicator Variogram\n",
"\n",
"The indicator variogram is calculated and modeled, since this is required input to calculation of the Gaussian variogram model in the next section (used for distance function $df$ modeling).\n",
"\n",
"### Apply the variogram object for convenience\n",
"Variogram calculation, modeling, plotting and checking are readily accomplished with the variogram object, although unprovided parameters are inferred."
]
},
{
"cell_type": "code",
"metadata": {
"ExecuteTime": {
"end_time": "2020-06-03T02:26:05.598130Z",
"start_time": "2020-06-03T02:26:05.593128Z"
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"outputId": "04b6a4ed-b179-4195-ed30-21e623bedf8f"
},
"source": [
"# get the proportions\n",
"proportion = sum(dat['Domain Indicator'])/len(dat)\n",
"print('Proportion of inside data: %.3f'%(proportion))\n",
"\n",
"variance = proportion - proportion**2"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Proportion of inside data: 0.764\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"ExecuteTime": {
"end_time": "2020-06-03T02:26:05.618132Z",
"start_time": "2020-06-03T02:26:05.602130Z"
},
"id": "riJbgcsigJzJ",
"colab_type": "code",
"colab": {}
},
"source": [
"# Perform data spacing analysis\n",
"dat.spacing(n_nearest=1)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"ExecuteTime": {
"end_time": "2020-06-03T02:26:05.635151Z",
"start_time": "2020-06-03T02:26:05.630128Z"
},
"id": "i_8MXY-GgJzL",
"colab_type": "code",
"colab": {},
"outputId": "0957ddfc-6273-4767-fc33-9ed77f4cfeb9"
},
"source": [
"lag_length = dat['Data Spacing (m)'].values.mean()\n",
"print('average data spacing in XY plane: {:.3f} {}'.format(lag_length,\n",
" gs.Parameters['plotting.unit']))"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"average data spacing in XY plane: 35.533 m\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"ExecuteTime": {
"end_time": "2020-06-03T02:26:05.647128Z",
"start_time": "2020-06-03T02:26:05.641132Z"
},
"id": "_CncEMC6gJzN",
"colab_type": "code",
"colab": {}
},
"source": [
"mean_range = (np.ptp(dat[dat.x].values) + np.ptp(dat[dat.y].values)) * 0.5\n",
"n_lag = np.ceil((mean_range * 0.5) / lag_length)\n",
"lag_tol = lag_length * 0.6"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"ExecuteTime": {
"end_time": "2020-06-03T02:26:05.955132Z",
"start_time": "2020-06-03T02:26:05.651162Z"
},
"id": "j_PirXoEgJzT",
"colab_type": "code",
"colab": {}
},
"source": [
"var_calc = gs.Program(program=exe_dir+'varcalc')"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"ExecuteTime": {
"end_time": "2020-06-03T02:26:06.102136Z",
"start_time": "2020-06-03T02:26:05.960131Z"
},
"id": "cuXLhpMtgJzV",
"colab_type": "code",
"colab": {},
"outputId": "f0796424-64aa-4fbd-8f76-34a05bc69eec"
},
"source": [
"parstr = \"\"\" Parameters for VARCALC\n",
" **********************\n",
" \n",
"START OF PARAMETERS:\n",
"{file} -file with data\n",
"2 3 0 - columns for X, Y, Z coordinates\n",
"1 4 - number of variables,column numbers (position used for tail,head variables below)\n",
"{t_min} 1.0e21 - trimming limits\n",
"{n_directions} -number of directions\n",
"0.0 90 1000 0.0 22.5 1000 0.0 -Dir 01: azm,azmtol,bandhorz,dip,diptol,bandvert,tilt\n",
" {n_lag} {lag_length} {lag_tol} - number of lags,lag distance,lag tolerance\n",
"{output} -file for experimental variogram points output.\n",
"0 -legacy output (0=no, 1=write out gamv2004 format)\n",
"1 -run checks for common errors\n",
"1 -standardize sills? (0=no, 1=yes)\n",
"1 -number of variogram types\n",
"1 1 10 1 {variance} -tail variable, head variable, variogram type (and cutoff/category), sill\n",
"\"\"\"\n",
"\n",
"n_directions = 1\n",
"varcalc_outfl = os.path.join(outdir, 'varcalc.out')\n",
"\n",
"var_calc.run(parstr=parstr.format(file=dat.flname,\n",
" n_directions = n_directions,\n",
" t_min = gs.Parameters['data.tmin'],\n",
" n_lag=n_lag,\n",
" lag_length = lag_length,\n",
" lag_tol = lag_tol,\n",
" variance = variance,\n",
" output=varcalc_outfl),\n",
" liveoutput=True)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Calling: ['../pygeostat/executable/varcalc', 'temp']\n",
"\n",
"varcalc version: 1.400\n",
"\n",
" data file: \n",
" c:\\users\\mostafahadavand\\anaconda3\\envs\\pygeostat_public\\lib\\site-packages\\pyge\n",
" ostat\\data\\example_data\\reservoir_boundary.dat\n",
" x,y,z columns: 2 3 0\n",
" number of variables: 1\n",
" Variable columns: 4\n",
" tmin,tmax: -998.000000000000 1.000000000000000E+021\n",
" number of directions: 1\n",
" direction parameters:\n",
" azm,azmtol,bandhorz 0.000000000000000E+000 90.0000000000000 \n",
" 1000.00000000000 \n",
" dip,diptol,bandvert 0.000000000000000E+000 22.5000000000000 \n",
" 1000.00000000000 \n",
" tilt 0.000000000000000E+000\n",
" nlags,lagdist,lagtol 16 35.5325195368409 \n",
" 21.3195117221045 \n",
" output file: Output/varcalc.out\n",
" legacy output? 0\n",
" run checks? 1\n",
" attempt to standardize sills? 1\n",
" number of variogram types: 1\n",
" Variogram tail,head,type 1 1 10\n",
" cutoff/category 1.00000000000000 \n",
" standardizing with sill = 0.180240836193861 \n",
" Reading data file\n",
" Setting up final parameters for variogram calculation\n",
" Calculating variograms...\n",
" working on direction 1\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"ExecuteTime": {
"end_time": "2020-06-03T02:26:06.240143Z",
"start_time": "2020-06-03T02:26:06.108155Z"
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"id": "ONJMSc1vgJzX",
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"outputId": "e35d253c-3003-4ac1-a519-7c56bcbf601e"
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"source": [
"varfl = gs.DataFile(varcalc_outfl)\n",
"varfl.head()"
],
"execution_count": null,
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{
"cell_type": "markdown",
"metadata": {
"id": "iEuXTOdegJzm",
"colab_type": "text"
},
"source": [
"## 4. Calculate and model the Gaussian Variogram\n",
"\n",
"The Gaussian variogram that yields the indicator variogram after truncation of a Gaussian random field is calculated. This Gaussian variogram is modeled and input to $df$ modeing. "
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "rm2NNil3gJzn",
"colab_type": "text"
},
"source": [
"#### Calculate the Gaussian variogram\n",
"The bigaus2 program applies the Gaussian integration method, given the indicator variogram and the proportion of the indicator."
]
},
{
"cell_type": "code",
"metadata": {
"ExecuteTime": {
"end_time": "2020-06-03T02:26:07.623133Z",
"start_time": "2020-06-03T02:26:07.480148Z"
},
"id": "p1KiwM6JgJzn",
"colab_type": "code",
"colab": {}
},
"source": [
"bigaus2 = gs.Program(exe_dir+'bigaus2')"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"ExecuteTime": {
"end_time": "2020-06-03T02:26:07.675130Z",
"start_time": "2020-06-03T02:26:07.625164Z"
},
"scrolled": false,
"id": "Kjv6W2MvgJzp",
"colab_type": "code",
"colab": {},
"outputId": "5aa97d66-e70f-4314-83b8-11f5ab1aa926"
},
"source": [
"parstr = \"\"\" Parameters for BIGAUS2\n",
" **********************\n",
"\n",
"START OF PARAMETERS:\n",
"1 -input mode (1) model or (2) variogram file\n",
"nofile.out -file for input variogram\n",
"{proportion} -threshold/proportion\n",
"2 -calculation mode (1) NS->Ind or (2) Ind->NS\n",
"{outfl} -file for output of variograms\n",
"1 -number of thresholds\n",
"{proportion} -threshold cdf values\n",
"1 {n_lag} -number of directions and lags\n",
"0 0.0 {lag_length} -azm(1), dip(1), lag(1)\n",
"{varstr}\n",
"\"\"\"\n",
"\n",
"with open(varmodelfit_outfl, 'r') as f:\n",
" varmodel_ = f.readlines()\n",
"varstr = ''''''\n",
"for line in varmodel_:\n",
" varstr += line\n",
"\n",
"pars = dict(proportion=proportion,\n",
" lag_length=lag_length,\n",
" n_lag=n_lag,\n",
" outfl= os.path.join(outdir, 'bigaus2.out'),\n",
" varstr=varstr)\n",
"bigaus2.run(parstr=parstr.format(**pars), nogetarg=True)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Calling: ['../pygeostat/executable/bigaus2', 'temp']\n",
"\r\n",
" BIGAUS2 Version: 2.200\r\n",
"\r\n",
" Which parameter file do you want to use?\r\n",
" input mode = 1\r\n",
" input file = nofile.out \r\n",
" threshold probability = 0.7641196 \r\n",
" calculation mode = 2\r\n",
" output file = Output/bigaus2.out \r\n",
" number of cutoffs = 1\r\n",
" cutoffs = 0.7641196 \r\n",
" ndir,nlag = 1 16\r\n",
" azm, dip, lag = 0.0000000E+00 0.0000000E+00 35.53252 \r\n",
" x,y,z offsets = 0.0000000E+00 35.53252 0.0000000E+00\r\n",
" nst, c0 = 2 5.0000001E-02\r\n",
" it,cc,ang[1,2,3] = 1 0.1270000 0.0000000E+00 0.0000000E+00\r\n",
" 0.0000000E+00\r\n",
" a1 a2 a3 = 674.4600 284.330000000000 284.330000000000 \r\n",
" it,cc,ang[1,2,3] = 1 0.8230000 0.0000000E+00 0.0000000E+00\r\n",
" 0.0000000E+00\r\n",
" a1 a2 a3 = 676.2400 568.660000000000 568.660000000000 \r\n",
"\r\n",
" BIGAUS2 Version: 2.200 Finished\r\n",
"\r\n",
"\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ZWLcKBB0gJzr",
"colab_type": "text"
},
"source": [
"### Data manipulation to handle an odd data format\n",
"The bigaus2 program outputs an odd (legacyish) variogram format, which must be translated to the standard Variogram format."
]
},
{
"cell_type": "code",
"metadata": {
"ExecuteTime": {
"end_time": "2020-06-03T02:26:07.702129Z",
"start_time": "2020-06-03T02:26:07.677131Z"
},
"id": "mA_rq2etgJzr",
"colab_type": "code",
"colab": {},
"outputId": "255708bb-1d9e-4347-edda-e56feb0c572d"
},
"source": [
"# Read in the data before demonstrating its present form\n",
"expvargs = gs.readvarg(os.path.join(outdir, 'bigaus2.out'), 'all')\n",
"expvargs.head()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
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