Multiple-point Geostatistics Stochastic Modeling with Training Images

Preface, vii

Acknowledgments, xi

Part I Concepts

I.1 Hiking in the Sierra Nevada, 3

I.2 Spatial estimation based on random function theory, 7

I.3 Universal kriging with training images, 29

I.4 Stochastic simulations based on random function theory, 49

I.5 Stochastic simulation without random function theory, 59

I.6 Returning to the Sierra Nevada, 75

Part II Methods

II.1 Introduction, 87

II.2 The algorithmic building blocks, 91

II.3 Multiple-point geostatistics algorithms, 155

II.4 Markov random fields, 173

II.5 Nonstationary modeling with training images, 183

II.6 Multivariate modeling with training images, 199

II.7 Training image construction, 221

II.8 Validation and quality control, 239

II.9 Inverse modeling with training images, 259

II.10 Parallelization, 295

Part III Applications

III.1 Reservoir forecasting – the West Coast of Africa (WCA) reservoir, 303

III.2 Geological resources modeling in mining, 329

Coauthored by Cristian P´erez, Julian M. Ortiz, & Alexandre Boucher

III.3 Climate modeling application – the case of the Murray–Darling

Basin, 345

Index, 361

Gregoire Mariethoz is Professor Assistant at the Faculty of Geosciences and Environment of the University of Lausanne, Switzerland, and Adjunct Senior Lecturer in Water Resources Engineering at the University of New South Wales, Australia.

Jef Caers is Professor of Energy Resources Engineering at Stanford University, USA and Director of the Stanford Center for Reservoir Forecasting. His previous book publications include Petroleum Geostatistics (SPE) and Modeling Uncertainty in the Earth Sciences (Wiley-Blackwell).