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Applied Geostatistics with SGeMS: A User's GuideBy Nicolas Remy, Alexandre Boucher, Jianbing Wu
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The Stanford Geostatistical Modeling Software (SGeMS) is an open-source computer package for solving problems involving spatially related variables. It provides geostatistics practitioners with a user-friendly interface, an interactive 3-D visualization, and a wide selection of algorithms. This practical book provides a step-by-step guide to using SGeMS algorithms. It explains the underlying theory, demonstrates their implementation, discusses their potential limitations, and helps the user make an informed decision about the choice of one algorithm over another. Users can complete complex tasks using the embedded scripting language, and new algorithms can be developed and integrated through the SGeMS plug-in mechanism. SGeMS was the first software to provide algorithms for multiple-point statistics, and the book presents a discussion of the corresponding theory and applications. Incorporating the full SGeMS software (now available from www.cambridge.org/9781107403246), this book is a useful user-guide for Earth Science graduates and researchers, as well as practitioners of environmental mining and petroleum engineering.
- Sales Rank: #1616008 in Books
- Published on: 2011-05-19
- Original language: English
- Number of items: 1
- Dimensions: 9.61" h x .59" w x 6.69" l, 1.01 pounds
- Binding: Paperback
- 286 pages
Review
'At last: here is a publisher who has prepared a thoroughly practical and well presented guide to geostatistics together with software in a form which can be run by most on their own computer.' Geoscientist
"feel that the book is useful as a user's manual for a resource analyst with some experience." - GSA
"I feel that the book is useful as a user's manual for a resource analyst with some experience..." - Douglas F. Hambley, Environmental and Engineering Geoscience, May 2012
About the Author
Nicolas Remy received a BS in Mathematics and Physics from Ecole Nationale Superieure des Mines, Nancy, France, a MS in Petroleum Engineering from Stanford University and a PhD in geostatistics from Stanford University. He is currently a Senior Statistician at Yahoo!, leading the Data Mining and User Behavior Modeling group for the Yahoo! Media and Yahoo Communications and Communities business units. His research interests include multiple-points statistics, machine learning, graph theory and data mining. Alexandre Boucher received a B.Eng. in geological engineering from the Ecole Polytechnique de Montreal, Montreal, QC, Canada, an M.Phil. degree from the University of Queensland, Brisbane, Australia, and a Ph.D. from Stanford University, Stanford, CA. He teaches geostatistics in the Department of Environmental Earth System Sciences, Stanford University. He has taught short courses on the subject in the US and Japan. His research interests include geostatistics, data integration, remote sensing, uncertainty modeling, machine learning and probabilistic modeling of spatio-temporal phenomena. Jianbing Wu is a reservoir engineer with the Applied Reservoir Engineering group at ConocoPhillips. His research focuses on static and dynamic reservoir modeling. He received his Ph.D. in Petroleum Engineering in 2007 from Stanford University, and his ME and BS degrees in Mechanical Engineering from University of Science and Technology of China. He is currently a member of SPE, IAMG and SEG.
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