Solved Problems in Geostatistics

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.

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.

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
Table of Contents provided by Publisher. All Rights Reserved.

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Solved Problems in Geostatistics

Summary

Author Biography

Table of Contents

Supplemental Materials

FREE SHIPPING

Rent More, Save More! Use code: BBRENTAL

5% off 1 book, 7% off 2 books, 10% off 3+ books

Solved Problems in Geostatistics

Summary

Author Biography

Table of Contents

Supplemental Materials

Digital License