Usage
  • 52 views
  • 71 downloads

Geostatistical Categorical Variable Modeling using Optimization Techniques with Truncated Plurigaussian Simulation

  • Author / Creator
    Sadeghi, Samaneh
  • 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.

  • Subjects / Keywords
  • Graduation date
    2017-06
  • Type of Item
    Thesis
  • Degree
    Doctor of Philosophy
  • DOI
    https://doi.org/10.7939/R3KK94Q8B
  • License
    This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
  • Language
    English
  • Institution
    University of Alberta
  • Degree level
    Doctoral
  • Department
    • Department of Civil and Environmental Engineering
  • Specialization
    • Mining Engineering
  • Supervisor / co-supervisor and their department(s)
    • Boisvert, Jeffery B ( Civil and Environmental Engineering)
  • Examining committee members and their departments
    • Deutsch, Clayton V (Civil and Environmental Engineering)
    • Yashar Pourrahimian (Civil and Environmental Engineering)
    • Leung, Juliana (Civil and Environmental Engineering)
    • Ye, Ming (Scientific Computing)
    • Boisvert, Jeffery B (Civil and Environmental Engineering)