Solved Problems in Geostatistics

by Leuangthong, Oy; Khan, K. Daniel; Deutsch, Clayton V.

• ISBN13: 9780470177921
• ISBN10: 0470177926
• Edition: 1st
• Format: Paperback
• Copyright: 2008-08-25
• Publisher: Wiley-Interscience

Summary

Solved Problems in Geostatistics brings together exercises and projects that demonstrate key principles and build strong bridges between theory and practice. Each chapter focuses on a comprehensive topic with examples and problems for a technologically evolving audience. Problems in each chapter are classified as analytical, numerical, or practical; exercises are identified as foundational, advanced, or esoteric. Objectives highlight important learning concepts. This hands-on, practical guide offers a supplement to any college-level geostatistics or spatial statistics course, a manual for a geostatistics lab course, or a reference for practicing statisticians.

Author Biography

Oy Leuangthong, PhD, is Assistant Professor in the Department of Civil and Environmental Engineering at the University of Alberta, Canada. Dr. Leuangthong conducts extensive research on the use of geostatistical methods for improving mineral resources and petroleum reservoirs.

K.Daniel Khan, PHD, is an Earth Scientist with Chevron Corporation, specializing in reservoir modeling. Dr. Khan's work focuses on modeling heterogeneity and fluid flow processes using geostatistical and inverse techniques.

Clayton V. Deutsch, PHD, is Professor in the Department of Civil and Environmental Engineering at the University of Alberta, where he is also Director of the School of Mining and Petroleum Engineering. He has over twenty years of experience in both academia and industry. Dr. Deutsch has published over 100 articles in his areas of research interest, which include modeling heterogeneity and uncertainty in petroleum reservoirs and mineral deposits.

Table of Contents

Preface and Acknowledgments

Introduction

Plan of this Book

The Premise of Geostatistics

Nomenclature

Getting Comfortable with Probabilities

Parametric Probability Distributions

Variance of Linear Combinations

Standardization and Probability Intervals

Obtaining Representative Distributions

Basic Declustering

Debiasing with Bivariate Glaussian Distribution

Comparison of Declustering Methods

Monte Carlo Simulation

Impact of the Central Limit Theorem

Bootstrap and Spatial Bootstrap

Transfer of Uncertainty

Variograms and Volume Variance

Geometric Anisotropy

Variogram Calculation

Variogram Modeling and Volume Variance

Kriging

Stationary Kriging

Nonstationary Kriging

Screening Effect of Kriging

Gaussian Simulation

Bivariate Gaussian Distribution

Conditioning by Kriging

Gaussian Simulation

Indicators

Variogram of Objects

Indicator Variograms and the Gaussian Distribution

Indicator Simulation for Categorical Data

Multiple Variables

Linear Model of Coregionalization

Gaussian Cosimulation

Multiscale Cokriging

Special Topics

Decision Making in the Presence of Uncertainty

Trend Model Construction

Multiple Point Statistics

Closing Remarks

Bibliography

Index

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