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).

"I benefited from this book and plan to keep it as a resource on my bookshelf. I recommend Multiple-point Geostatistics: Stochastic Modeling with Training Images to my peers in mathematical geosciences." (Mathematical Geosciences, 2016)

• Preface, viiAcknowledgments, xiPart I ConceptsI.1 Hiking in the Sierra Nevada, 3I.2 Spatial estimation based on random function theory, 7I.3 Universal kriging with training images, 29I.4 Stochastic simulations based on random function theory, 49I.5 Stochastic simulation without random function theory, 59I.6 Returning to the Sierra Nevada, 75Part II MethodsII.1 Introduction, 87II.2 The algorithmic building blocks, 91II.3 Multiple-point geostatistics algorithms, 155II.4 Markov random fields, 173II.5 Nonstationary modeling with training images, 183II.6 Multivariate modeling with training images, 199II.7 Training image construction, 221II.8 Validation and quality control, 239II.9 Inverse modeling with training images, 259II.10 Parallelization, 295Part III ApplicationsIII.1 Reservoir forecasting – the West Coast of Africa (WCA) reservoir, 303III.2 Geological resources modeling in mining, 329Coauthored by Cristian P´erez, Julian M. Ortiz, & Alexandre BoucherIII.3 Climate modeling application – the case of the Murray–DarlingBasin, 345Index, 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).

"I benefited from this book and plan to keep it as a resource on my bookshelf. I recommend Multiple-point Geostatistics: Stochastic Modeling with Training Images to my peers in mathematical geosciences." (Mathematical Geosciences, 2016)

• Preface, viiAcknowledgments, xiPart I ConceptsI.1 Hiking in the Sierra Nevada, 3I.2 Spatial estimation based on random function theory, 7I.3 Universal kriging with training images, 29I.4 Stochastic simulations based on random function theory, 49I.5 Stochastic simulation without random function theory, 59I.6 Returning to the Sierra Nevada, 75Part II MethodsII.1 Introduction, 87II.2 The algorithmic building blocks, 91II.3 Multiple-point geostatistics algorithms, 155II.4 Markov random fields, 173II.5 Nonstationary modeling with training images, 183II.6 Multivariate modeling with training images, 199II.7 Training image construction, 221II.8 Validation and quality control, 239II.9 Inverse modeling with training images, 259II.10 Parallelization, 295Part III ApplicationsIII.1 Reservoir forecasting – the West Coast of Africa (WCA) reservoir, 303III.2 Geological resources modeling in mining, 329Coauthored by Cristian P´erez, Julian M. Ortiz, & Alexandre BoucherIII.3 Climate modeling application – the case of the Murray–DarlingBasin, 345Index, 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).

"I benefited from this book and plan to keep it as a resource on my bookshelf. I recommend Multiple-point Geostatistics: Stochastic Modeling with Training Images to my peers in mathematical geosciences." (Mathematical Geosciences, 2016)

• Preface, viiAcknowledgments, xiPart I ConceptsI.1 Hiking in the Sierra Nevada, 3I.2 Spatial estimation based on random function theory, 7I.3 Universal kriging with training images, 29I.4 Stochastic simulations based on random function theory, 49I.5 Stochastic simulation without random function theory, 59I.6 Returning to the Sierra Nevada, 75Part II MethodsII.1 Introduction, 87II.2 The algorithmic building blocks, 91II.3 Multiple-point geostatistics algorithms, 155II.4 Markov random fields, 173II.5 Nonstationary modeling with training images, 183II.6 Multivariate modeling with training images, 199II.7 Training image construction, 221II.8 Validation and quality control, 239II.9 Inverse modeling with training images, 259II.10 Parallelization, 295Part III ApplicationsIII.1 Reservoir forecasting – the West Coast of Africa (WCA) reservoir, 303III.2 Geological resources modeling in mining, 329Coauthored by Cristian P´erez, Julian M. Ortiz, & Alexandre BoucherIII.3 Climate modeling application – the case of the Murray–DarlingBasin, 345Index, 361