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Cover Art
E-RESOURCE
Author Azev, Leonardo, author.

Title Geostatistical methods for reservoir geophysics / Leonardo Azevedo, Amílcar Soares.

Published Cham, Switzerland : Springer, [2017]

Copies

Location Call No. Status
 UniM INTERNET resource    AVAILABLE
Physical description 1 online resource.
Series Advances in oil and gas exploration & production
Advances in oil and gas exploration & production.
Springer Earth and Environmental Science eBooks 2017 English+International
Bibliography Includes bibliographical references and index.
Contents Preface; Contents; List of Abbreviations and MathematicalSymbols; List of Figures; List of Tables; 1 Introduction-Geostatistical Methods for Integrating Seismic Reflection Data into Subsurface Earth Models; 1.1 Spatial Resolution Gap; 1.2 Seismic Inversion; 2 Fundamental Geostatistical Tools for Data Integration; 2.1 Spatial Continuity Patterns Analysis and Modeling; 2.1.1 Bi-point Statistics; 2.1.2 Complex Morphologic Patterns: Auxiliary and Reference Images; 2.1.3 Spatial Random Fields; 2.1.4 Variograms and Spatial Covariances; 2.1.5 Spatial Representativeness of the Variogram.
2.1.6 Spatial Continuity for Multivariate Systems2.1.7 Variogram Modeling Workflow; 2.1.8 Theoretical Variogram Models; 2.1.9 Linear Combinations of Variogram Models: Imbricated Structures; 2.1.10 Co-regionalized Models of Multivariate Systems; 2.2 Estimation Models; 2.2.1 Linear Estimation of Local Statistics; 2.2.2 Probabilistic Model of the Geostatistical Linear Estimator; 2.3 Kriging Estimate; 2.3.1 Kriging System Resolution; 2.4 Linear Estimation of Non-stationary Phenomena: Simple Kriging; 2.5 Co-kriging Estimate.
2.6 Co-estimation with a Secondary Variable in a Much Denser Sample Grid: Collocated Co-kriging2.7 Estimation of Local Probability Distribution Functions; 2.7.1 Gaussian Transform of the Experimental Data; 2.8 Estimation of Categorical Variables; 3 Simulation Models of Physical Phenomena in Earth Sciences; 3.1 Stochastic Simulation Models; 3.2 Sequential Simulation Models; 3.3 Sequential Gaussian Simulation; 3.4 Direct Sequential Simulation from Experimental Distributions; 3.4.1 Direct Sequential Simulation; 3.4.2 Direct Sequential Co-simulation.
3.4.3 Stochastic Sequential Co-simulation with Joint Probability Distributions3.4.4 Stochastic Simulation with Uncertain Data: DSS with Point Probability Distributions; 3.5 Simulation of Categorical Variables; 3.5.1 Indicator Simulation; 3.5.2 Alternative Simulation Methods for Categorical Variables; 3.5.3 High-Order Stochastic Simulation of Categorical Variables; 4 Integration of Geophysical Data for Reservoir Modeling and Characterization; 4.1 Seismic Inversion; 4.2 Bayesian Framework for Integrating Seismic Reflection Data into Subsurface Earth Models.
4.3 Iterative Geostatistical Seismic Inversion Methodologies4.3.1 Frequency Domain of Geostatistical Seismic Inversion; 4.3.2 Trace-by-Trace Geostatistical Seismic Inversion Methodologies; 4.3.3 Global Geostatistical Seismic Inversion Methodologies; 4.3.4 Global Geostatistical Acoustic Inversion; 4.3.5 Global Geostatistical Elastic Inversion; 4.3.6 Geostatistical Seismic AVA Inversion; 4.3.7 Application Example with Geostatistical Seismic AVA Inversion; 4.3.8 Seismic Inversion with Structural Local Models.
Summary This book presents a geostatistical framework for data integration into subsurface Earth modeling. It offers extensive geostatistical background information, including detailed descriptions of the main geostatistical tools traditionally used in Earth related sciences to infer the spatial distribution of a given property of interest. This framework is then directly linked with applications in the oil and gas industry and how it can be used as the basis to simultaneously integrate geophysical data (e.g. seismic reflection data) and well-log data into reservoir modeling and characterization. All of the cutting-edge methodologies presented here are first approached from a theoretical point of view and then supplemented by sample applications from real case studies involving different geological scenarios and different challenges. The book offers a valuable resource for students who are interested in learning more about the fascinating world of geostatistics and reservoir modeling and characterization. It offers them a deeper understanding of the main geostatistical concepts and how geostatistics can be used to achieve better data integration and reservoir modeling.
Other author Soares, Amilcar, author.
SpringerLink issuing body.
Subject Petroleum -- Geology -- Statistical methods.
Petroleum -- Geology -- Mathematical models.
Prospecting -- Geophysical methods.
Gas reservoirs.
Oil industries -- Statistics.
Gas industry -- Statistics.
Electronic books.
Statistics.
ISBN 9783319532011 (electronic bk.)
3319532014 (electronic bk.)
9783319532004
3319532006
Standard Number 10.1007/978-3-319-53201-1