Multiple-point Geostatistics - Stochastic Modeling with Training Images
Published on 30. October 2014
Software
Other digital
376 pages
978-1-118-66295-3 (ISBN)
Description
This book provides a comprehensive introduction to multiple-point geostatistics, where spatial continuity is described using training images. Multiple-point geostatistics aims at bridging the gap between physical modelling/realism and spatio-temporal stochastic modelling. The book provides an overview of this new field in three parts. Part I presents a conceptual comparison between traditional random function theory and stochastic modelling based on training images, where random function theory is not always used. Part II covers in detail various algorithms and methodologies starting from basic building blocks in statistical science and computer science. Concepts such as non-stationary and multi-variate modeling, consistency between data and model, the construction of training images and inverse modelling are treated. Part III covers three example application areas, namely, reservoir modelling, mineral resources modelling and climate model downscaling. This book will be an invaluable reference for students, researchers and practitioners of all areas of the Earth Sciences where forecasting based on spatio-temporal data is performed.
More details
Language
English
Place of publication
New York
United States
Target group
Professional and scholarly
Dimensions
Height: 229 mm
Width: 152 mm
Thickness: 15 mm
Weight
666 gr
ISBN-13
978-1-118-66295-3 (9781118662953)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
E-Book
10/2014
Wiley-Blackwell
€96.99
Available for download
E-Book
10/2014
Wiley-Blackwell
€96.99
Available for download
Person
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).
Content
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