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Geostatistical Categorical Variable Modeling using Optimization Techniques with Truncated Plurigaussian Simulation Open Access


Other title
Mask Optimization in TPG Simulation
Multidimensional Scaling (MDS) Mask
Transition Probabilities in TPG Simulation
Variogram Optimization in TPG Simulation
Threshold based Mask
Geostatistical Categorical Variable Modeling
Truncated Plurigaussian Simulation
TPG Simulation with Locally Varying Proportions
Type of item
Degree grantor
University of Alberta
Author or creator
Sadeghi, Samaneh
Supervisor and department
Boisvert, Jeffery B ( Civil and Environmental Engineering)
Examining committee member and department
Leung, Juliana (Civil and Environmental Engineering)
Yashar Pourrahimian (Civil and Environmental Engineering)
Ye, Ming (Scientific Computing)
Deutsch, Clayton V (Civil and Environmental Engineering)
Boisvert, Jeffery B (Civil and Environmental Engineering)
Department of Civil and Environmental Engineering
Mining Engineering
Date accepted
Graduation date
2017-06:Spring 2017
Doctor of Philosophy
Degree level
Stochastic simulation of rock types is a major area of continuing research. Rock types account for the majority of heterogeneity in mineral/ petroleum deposits, and a good understanding of the geometry and spatial distribution of natural phenomena is required for reliable resource and reserve estimation. Integrating geological concepts in geostatistical modeling is important in order to provide realistic geological models. The resulting models are practical and reasonable if they provide a reliable reproduction of the true underlying structure of the subsurface. A methodology has been developed to construct geostatistical models that reproduce features inferred from data and are consistent with geologic understanding of the deposit. The main idea is to transfer essential geological features to the geostatistical models, particularly when there is a clear ordering between categories. In deposits with complicated ordering structures, truncated plurigaussian simulation (TPG) is a flexible method for simulating facies categories. This is a modeling technique which relies on simulating multiple underlying Gaussian variables to represent a categorical variable. TPG simulation utilizes truncation masks for mapping categorical variables to a continuous space. Therefore, an optimized truncation mask is required to best represent the contacts and transitions between facies categories with respect to the geological interpretations. In addition, continuity of the categories is controlled by the variogram of each Gaussian variable. Finding the variogram model of the underlying Gaussian variable is essential to reproduce the target indicator variograms of facies categories and is an important challenge of TPG simulation. TPG simulation is improved through adopting optimization techniques for modeling the underlying Gaussian variables and the categorical variable. A novel technique is developed which allows for the reproduction of the spatial continuity of facies categories by automatically inferring the optimum variogram models for the underlying Gaussian deviates. Implementation of this methodology demonstrates improvement in the modeling of complicated geologic features and in accounting for changes in the categories proportions. Reasonable reproductions of the transitions observed in the data as well as the categorical data observations from the production data are achieved.
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