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E-raamat: Applied Geostatistics with SGeMS: A User's Guide

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, , (Stanford University, California)
  • Formaat: EPUB+DRM
  • Ilmumisaeg: 14-Apr-2011
  • Kirjastus: Cambridge University Press
  • Keel: eng
  • ISBN-13: 9781139234795
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Applied Geostatistics with SGeMS: A User's GuideSuurem pilt
  • Formaat: EPUB+DRM
  • Ilmumisaeg: 14-Apr-2011
  • Kirjastus: Cambridge University Press
  • Keel: eng
  • ISBN-13: 9781139234795
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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.

This practical book provides a detailed guide to using algorithms from the Stanford Geostatistical Modeling Software (SGeMS), an open-source computer package for solving problems involving spatially related variables. Accompanied by a CD with the software, it's a useful user-guide for Earth Science graduates, and practitioners of environmental and petroleum engineering.

Arvustused

Review of the hardback: '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

Muu info

A step-by-step user guide to geostatistical modeling for Earth Science graduates and researchers, and professional practitioners.
Foreword ix
Albert Tarantola
Preface xi
List of programs
xiii
List of symbols
xv
1 Introduction
1(4)
2 General overview
5(24)
2.1 A quick tour of the graphical user interface
5(1)
2.2 A typical geostatistical analysis using SGeMS
5(18)
2.2.1 Loading data into an SGeMS project
8(2)
2.2.2 Exploratory data analysis (EDA)
10(1)
2.2.3 Variogram modeling
10(2)
2.2.4 Creating a grid
12(1)
2.2.5 Running a geostatistics algorithm
13(1)
2.2.6 Displaying the results
14(5)
2.2.7 Post-processing the results with Python
19(2)
2.2.8 Saving the results
21(1)
2.2.9 Automating tasks
21(2)
2.3 Data file formats
23(1)
2.4 Parameter files
24(2)
2.5 Defining a 3D ellipsoid
26(3)
3 Geostatistics: a recall of concepts
29(51)
3.1 Random variable
30(3)
3.2 Random function
33(5)
3.2.1 Simulated realizations
34(3)
3.2.2 Estimated maps
37(1)
3.3 Conditional distributions and simulations
38(6)
3.3.1 Sequential simulation
40(2)
3.3.2 Estimating the local conditional distributions
42(2)
3.4 Inference and stationarity
44(4)
3.5 The variogram, a 2-point statistics
48(2)
3.6 The kriging paradigm
50(12)
3.6.1 Simple kriging
51(3)
3.6.2 Ordinary kriging and other variants
54(3)
3.6.3 Kriging with linear average variable
57(2)
3.6.4 Cokriging
59(2)
3.6.5 Indicator kriging
61(1)
3.7 An introduction to mp statistics
62(3)
3.8 Two-point simulation algorithms
65(6)
3.8.1 Sequential Gaussian simulation
66(1)
3.8.2 Direct sequential simulation
67(1)
3.8.3 Direct error simulation
68(1)
3.8.4 Indicator simulation
69(2)
3.9 Multiple-point simulation algorithms
71(3)
3.9.1 Single normal equation simulation (SNESIM)
71(1)
3.9.2 Filter-based algorithm (FILTERSIM)
72(2)
3.10 The nu/tau expression for combining conditional probabilities
74(5)
3.11 Inverse problem
79(1)
4 Data sets and SGeMS EDA tools
80(10)
4.1 The data sets
80(4)
4.1.1 The 2D data set
80(1)
4.1.2 The 3D data set
81(3)
4.2 The SGeMS EDA tools
84(6)
4.2.1 Common parameters
85(1)
4.2.2 Histogram
85(2)
4.2.3 Q-Q plot and P-P plot
87(1)
4.2.4 Scatter plot
87(3)
5 Variogram computation and modeling
90(11)
5.1 Variogram computation in SGeMS
92(6)
5.1.1 Selecting the head and tail properties
92(1)
5.1.2 Computation parameters
93(5)
5.1.3 Displaying the computed variograms
98(1)
5.2 Variogram modeling in SGeMS
98(3)
6 Common parameter input interfaces
101(8)
6.1 Algorithm panel
101(1)
6.2 Selecting a grid and property
102(1)
6.3 Selecting multiple properties
103(1)
6.4 Search neighborhood
104(1)
6.5 Variogram
104(1)
6.6 Kriging
105(1)
6.7 Line entry
105(1)
6.8 Non-parametric distribution
106(2)
6.9 Errors in parameters
108(1)
7 Estimation algorithms
109(23)
7.1 KRIGING: univariate kriging
109(4)
7.2 INDICATOR KRIGING
113(6)
7.3 COKRIGING: kriging with secondary data
119(3)
7.4 BKRIG: block kriging estimation
122(10)
8 Stochastic simulation algorithms
132(83)
8.1 Variogram-based simulations
132(36)
8.1.1 LUSIM: LU simulation
133(2)
8.1.2 SGSIM: sequential Gaussian simulation
135(4)
8.1.3 COSGSIM: sequential Gaussian co-simulation
139(4)
8.1.4 DSSIM: direct sequential simulation
143(4)
8.1.5 SISIM: sequential indicator simulation
147(6)
8.1.6 COSISIM: sequential indicator co-simulation
153(4)
8.1.7 BSSIM: block sequential simulation
157(6)
8.1.8 BESIM: block error simulation
163(5)
8.2 Multiple-point simulation algorithms
168(47)
8.2.1 SNESIM: single normal equation simulation
169(22)
8.2.2 FILTERSIM: filter-based simulation
191(24)
9 Utilities
215(30)
9.1 TRANS: histogram transformation
215(3)
9.2 TRANSCAT: categorical transformation
218(4)
9.3 Postkriging: post-processing of kriging estimates
222(2)
9.4 Postsim: post-processing of realizations
224(3)
9.5 Nu-Tau Model: combining probability fields
227(1)
9.6 Bcovar: block covariance calculation
228(5)
9.7 Image Processing
233(1)
9.8 Moving Window: moving window statistics
234(3)
9.9 Tigenerator: Object-based image generator
237(8)
9.9.1 Object interaction
239(6)
10 Scripting, commands and plug-ins
245(9)
10.1 Commands
245(4)
10.1.1 Command lists
246(2)
10.1.2 Execute command file
248(1)
10.2 Python script
249(3)
10.2.1 SGeMS Python modules
250(1)
10.2.2 Running Python scripts
250(2)
10.3 Plug-ins
252(2)
Bibliography 254(6)
Index 260
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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