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Solved Problems in Geostatistics [Pehme köide]

(University of Alberta, Canada), (University of Alberta, Canada), (Chevron Energy Technology Company)
  • Formaat: Paperback / softback, 216 pages, kõrgus x laius x paksus: 255x181x15 mm, kaal: 393 g, Charts: 10 B&W, 0 Color; Photos: 0 B&W, 0 Color; Drawings: 22 B&W, 0 Color; Maps: 0 B&W, 0 Color; Tables: 0 B&W, 0 Color; Graphs: 22 B&W, 0 Color
  • Ilmumisaeg: 12-Sep-2008
  • Kirjastus: Wiley-Interscience
  • ISBN-10: 0470177926
  • ISBN-13: 9780470177921
Teised raamatud teemal:
  • Formaat: Paperback / softback, 216 pages, kõrgus x laius x paksus: 255x181x15 mm, kaal: 393 g, Charts: 10 B&W, 0 Color; Photos: 0 B&W, 0 Color; Drawings: 22 B&W, 0 Color; Maps: 0 B&W, 0 Color; Tables: 0 B&W, 0 Color; Graphs: 22 B&W, 0 Color
  • Ilmumisaeg: 12-Sep-2008
  • Kirjastus: Wiley-Interscience
  • ISBN-10: 0470177926
  • ISBN-13: 9780470177921
Teised raamatud teemal:
This unique book presents a learn-by-doing introduction to geostatistics. Geostatistics provides the essential numerical tools for addressing research problems that are encountered in fields of study such as geology, engineering, and the earth sciences. Illustrating key methods through both theoretical and practical exercises, Solved Problems in Geostatistics is a valuable and well-organized collection of worked-out problems that allow the reader to master the statistical techniques for modeling data in the geological sciences.

The book's scope of coverage begins with the elements from statistics and probability that form the foundation of most geostatistical methodologies, such as declustering, debiasing methods, and Monte Carlo simulation. Next, the authors delve into three fundamental areas in conventional geostatistics: covariance and variogram functions; kriging; and Gaussian simulation. Finally, special topics are introduced through problems involving utility theory, loss functions, and multiple-point geostatistics.

Each topic is treated in the same clearly organized format. First, an objective presents the main concepts that will be established in the section. Next, the background and assumptions are outlined, supplying the comprehensive foundation that is necessary to begin work on the problem. A solution plan demonstrates the steps and considerations that have to be taken when working with the exercise, and the solution allows the reader to check their work. Finally, a remarks section highlights the overarching principles and noteworthy aspects of the problem.

Additional exercises are available via a related Web site, which also includes data related to the book problems and software programs that facilitate their resolution. Enforcing a truly hands-on approach to the topic, Solved Problems in Geostatistics is an indispensable supplement for courses on geostatistics and spatial statistics a the upper-undergraduate and graduate levels.It also serves as an applied reference for practicing professionals in the geosciences.

Arvustused

?The book is more than its title; it really is a treatise on how to model data by two experienced and competent analysts.?(Biometrics , September 2009)

Preface and Acknowledgments vii
Introduction
1(6)
Plan of this Book
2(2)
The Premise of Geostatistics
4(2)
Nomenclature
6(1)
Getting Comfortable with Probabilities
7(14)
Parametric Probability Distributions
7(6)
Variance of Linear Combinations
13(3)
Standardization and Probability Intervals
16(5)
Obtaining Representative Distributions
21(20)
Basic Declusterring
22(6)
Debiasing With Bivariate Gaussian Distribution
28(7)
Comparison of Declustering Methods
35(6)
Monte Carlo Simulation
41(18)
Impact of the Central Limit theorem
43(4)
Bootstrap and Spatial Bootstrap
47(7)
Transfer of Uncertainty
54(5)
Variograms and Volume Variance
59(26)
Geometric Anisotropy
60(9)
Variogram Calculation
69(9)
Variogram Modeling and Volume Variance
78(7)
Kriging
85(18)
Stationary Kriging
86(6)
Nonstationary Kriging
92(5)
Screening Effect of Kriging
97(6)
Gaussian simulation
103(22)
Bivariate Gaussian Distribution
104(6)
Conditioning by Kriging
110(7)
Gaussian Simulation
117(8)
Indicators
125(18)
Variogram of Objects
127(6)
Indicator Variograms and the Gaussian distribution
133(4)
Indicator Simulation for Categorical Data
137(6)
Multiple Variables
143(24)
Linear Model of Coregionalization
144(5)
Gaussian Cosimulation
149(10)
Multiscale Cokriging
159(8)
Special Topics
167(26)
Decision Making in the Presence of Uncertainty
168(7)
Trend Model Construction
175(7)
Multiple Point Statistics
182(11)
Closing Remarks
193(4)
Bibliography 197(8)
Index 205
Oy Leuangthong, PhD, is Assistant Professor in the Department of Civil and Environmental Engineering at the University of Alberta, Canada. Dr. Leuangthong conducts extensive research on the use of geostatistical methods for improving mineral resources and petroleum reservoirs. K.Daniel Khan, PHD, is an Earth Scientist with Chevron Corporation, specializing in reservoir modeling. Dr. Khan's work focuses on modeling heterogeneity and fluid flow processes using geostatistical and inverse techniques.

Clayton V. Deutsch, PHD, is Professor in the Department of Civil and Environmental Engineering at the University of Alberta, where he is also Director of the School of Mining and Petroleum Engineering. He has over twenty years of experience in both academia and industry. Dr. Deutsch has published over 100 articles in his areas of research interest, which include modeling heterogeneity and uncertainty in petroleum reservoirs and mineral deposits.