Tutorial on Boolean Model¶
This file is meant to demonstrate the use of gstlearn for Boolean Model
In [1]:
import numpy as np
import gstlearn as gl
import gstlearn.plot as gp
import gstlearn.document as gdoc
import random as rnd
gdoc.setNoScroll()
Setting some global variables
In [2]:
# Set the Global Options
verbose = True
flagGraphic = True
# Define the Space Dimension
ndim = 2
gl.defineDefaultSpace(gl.ESpaceType.RN, ndim)
# Set the Seed for the Random Number generator
gl.law_set_random_seed(5584)
rnd.seed(13155)
In [3]:
nxcell = 100
grid = gl.DbGrid.create([nxcell, nxcell])
grid.display();
Data Base Grid Characteristics ============================== Data Base Summary ----------------- File is organized as a regular grid Space dimension = 2 Number of Columns = 3 Total number of samples = 10000 Grid characteristics: --------------------- Origin : 0.000 0.000 Mesh : 1.000 1.000 Number : 100 100 Variables --------- Column = 0 - Name = rank - Locator = NA Column = 1 - Name = x1 - Locator = x1 Column = 2 - Name = x2 - Locator = x2
We now establish the stationary Boolean Model
In [4]:
tokens = gl.ModelBoolean(0.01, True)
We must first define the dictionary of shapes used in the Model, composed of
- Ellipsoids
- parallelelipieds
In [5]:
token_ellipsoid = gl.ShapeEllipsoid(0.4, 10.0, 20.0, 2.0)
token_ellipsoid.setFactorX2Y(1.5)
tokens.addToken(token_ellipsoid)
token_parallelepiped = gl.ShapeParallelepiped(0.6, 5, 7.0, 1.0)
tokens.addToken(token_parallelepiped)
tokens.display()
Object Model ============ - Poisson Intensity = 0.01 Token 1 ------- Full Ellipsoid - Proportion=0.4 - X-Extension: Constant=10 - Y-Extension: Constant=20 - Z-Extension: Constant=2 - Orientation Angle: Constant=0 Y-Extension = X_Extension * 1.5 Token 2 ------- Parallelepiped - Proportion=0.6 - X-Extension: Constant=5 - Y-Extension: Constant=7 - Z-Extension: Constant=1 - Orientation Angle: Constant=0
Non conditional simulation¶
In [6]:
err = gl.simbool(None, grid, tokens, namconv=gl.NamingConvention("NC"))
grid
Out[6]:
Data Base Grid Characteristics ============================== Data Base Summary ----------------- File is organized as a regular grid Space dimension = 2 Number of Columns = 5 Total number of samples = 10000 Grid characteristics: --------------------- Origin : 0.000 0.000 Mesh : 1.000 1.000 Number : 100 100 Variables --------- Column = 0 - Name = rank - Locator = NA Column = 1 - Name = x1 - Locator = x1 Column = 2 - Name = x2 - Locator = x2 Column = 3 - Name = NC.Facies - Locator = NA Column = 4 - Name = NC.Rank - Locator = NA
We have created two new variables in the output Grid file:
- the Facies
- the Rank of the object
In [7]:
gp.plot(grid, "NC.Facies")
gp.decoration(title="Facies")
In [8]:
gp.plot(grid, "NC.Rank", flagLegend=True)
gp.decoration(title="Rank")
Conditional simulation¶
Few conditioning points¶
We sample the grid in order to create a new Point data base: the number of selected points is small.
In [9]:
ndat = 20
randomlist = rnd.sample(range(0, grid.getNSample()), ndat)
data = gl.Db.createReduce(grid, ranks=randomlist)
In [10]:
gp.plot(grid, "NC.Facies")
gp.plot(data, nameColor="NC.Facies")
gp.decoration(title="Conditioning Information")
We must slightly transform the facies information that will serve as conditioning
In [11]:
data["Facies1"] = np.nan_to_num(data["NC.Facies"], nan=0.0)
data.setLocator("Facies1", gl.ELoc.Z, cleanSameLocator=True)
We can now launch a conditional simulation
In [12]:
err = gl.simbool(data, grid, tokens, verbose=True, namconv=gl.NamingConvention("CD1"))
grid
Boolean simulation ================== - Conditioning option = YES Simulating the initial tokens ----------------------------- - Number of grains to be covered = 9 - Number of conditioning pores = 11 - Number of Initial Objects = 8 - Number of iterations = 8 Simulating the secondary tokens ------------------------------- - Ending number of primary objects = 0 - Total number of objects = 93
Out[12]:
Data Base Grid Characteristics ============================== Data Base Summary ----------------- File is organized as a regular grid Space dimension = 2 Number of Columns = 7 Total number of samples = 10000 Grid characteristics: --------------------- Origin : 0.000 0.000 Mesh : 1.000 1.000 Number : 100 100 Variables --------- Column = 0 - Name = rank - Locator = NA Column = 1 - Name = x1 - Locator = x1 Column = 2 - Name = x2 - Locator = x2 Column = 3 - Name = NC.Facies - Locator = NA Column = 4 - Name = NC.Rank - Locator = NA Column = 5 - Name = CD1.Facies1.Facies - Locator = NA Column = 6 - Name = CD1.Facies1.Rank - Locator = NA
In [13]:
gp.plot(grid, "CD1.Facies1.Facies")
gp.plot(data, nameColor="Facies1")
gp.decoration(title="Conditional Simulation (Small)")
Lots of conditioning points¶
We now sample the grid in order to create a lot of conditioning samples
In [14]:
ndat = 20
randomlist = rnd.sample(range(0, grid.getNSample()), ndat)
data = gl.Db.createReduce(grid, ranks=randomlist)
In [15]:
data["Facies2"] = np.nan_to_num(data["NC.Facies"], nan=0.0)
data.setLocator("Facies2", gl.ELoc.Z, cleanSameLocator=True)
In [16]:
gp.plot(grid, "NC.Facies")
gp.plot(data, nameColor="NC.Facies")
gp.decoration(title="Conditioning Information (Large)")
In [17]:
err = gl.simbool(data, grid, tokens, verbose=True, namconv=gl.NamingConvention("CD2"))
grid
Boolean simulation ================== - Conditioning option = YES Simulating the initial tokens ----------------------------- - Number of grains to be covered = 13 - Number of conditioning pores = 7 - Number of Initial Objects = 12 - Number of iterations = 14 Simulating the secondary tokens ------------------------------- - Ending number of primary objects = 0 - Total number of objects = 96
Out[17]:
Data Base Grid Characteristics ============================== Data Base Summary ----------------- File is organized as a regular grid Space dimension = 2 Number of Columns = 9 Total number of samples = 10000 Grid characteristics: --------------------- Origin : 0.000 0.000 Mesh : 1.000 1.000 Number : 100 100 Variables --------- Column = 0 - Name = rank - Locator = NA Column = 1 - Name = x1 - Locator = x1 Column = 2 - Name = x2 - Locator = x2 Column = 3 - Name = NC.Facies - Locator = NA Column = 4 - Name = NC.Rank - Locator = NA Column = 5 - Name = CD1.Facies1.Facies - Locator = NA Column = 6 - Name = CD1.Facies1.Rank - Locator = NA Column = 7 - Name = CD2.Facies2.Facies - Locator = NA Column = 8 - Name = CD2.Facies2.Rank - Locator = NA
In [18]:
gp.plot(grid, "CD2.Facies2.Facies")
gp.plot(data, nameColor="Facies2")
gp.decoration(title="Conditional Simulation (Large)")
