Geostatistical Reservoir Modeling

Geostatistical Reservoir Modeling
Author :
Publisher : Oxford University Press
Total Pages : 449
Release :
ISBN-10 : 9780199358830
ISBN-13 : 0199358834
Rating : 4/5 (30 Downloads)

Book Synopsis Geostatistical Reservoir Modeling by : Michael J. Pyrcz

Download or read book Geostatistical Reservoir Modeling written by Michael J. Pyrcz and published by Oxford University Press. This book was released on 2014-04-16 with total page 449 pages. Available in PDF, EPUB and Kindle. Book excerpt: Published in 2002, the first edition of Geostatistical Reservoir Modeling brought the practice of petroleum geostatistics into a coherent framework, focusing on tools, techniques, examples, and guidance. It emphasized the interaction between geophysicists, geologists, and engineers, and was received well by professionals, academics, and both graduate and undergraduate students. In this revised second edition, Deutsch collaborates with co-author Michael Pyrcz to provide an expanded (in coverage and format), full color illustrated, more comprehensive treatment of the subject with a full update on the latest tools, methods, practice, and research in the field of petroleum Geostatistics. Key geostatistical concepts such as integration of geologic data and concepts, scale considerations, and uncertainty models receive greater attention, and new comprehensive sections are provided on preliminary geological modeling concepts, data inventory, conceptual model, problem formulation, large scale modeling, multiple point-based simulation and event-based modeling. Geostatistical methods are extensively illustrated through enhanced schematics, work flows and examples with discussion on method capabilities and selection. For example, this expanded second edition includes extensive discussion on the process of moving from an inventory of data and concepts through conceptual model to problem formulation to solve practical reservoir problems. A greater number of examples are included, with a set of practical geostatistical studies developed to illustrate the steps from data analysis and cleaning to post-processing, and ranking. New methods, which have developed in the field since the publication of the first edition, are discussed, such as models for integration of diverse data sources, multiple point-based simulation, event-based simulation, spatial bootstrap and methods to summarize geostatistical realizations.


Geostatistical Reservoir Modeling Related Books

Geostatistical Reservoir Modeling
Language: en
Pages: 449
Authors: Michael J. Pyrcz
Categories: Mathematics
Type: BOOK - Published: 2014-04-16 - Publisher: Oxford University Press

DOWNLOAD EBOOK

Published in 2002, the first edition of Geostatistical Reservoir Modeling brought the practice of petroleum geostatistics into a coherent framework, focusing on
Geostatistical Reservoir Modeling
Language: en
Pages: 449
Authors: Michael J. Pyrcz
Categories: Mathematics
Type: BOOK - Published: 2014-05 - Publisher: Oxford University Press

DOWNLOAD EBOOK

A revised edition that provides a full update on the most current methods, tools, and research in petroleum geostatistics.
Geostatistical Reservoir Modeling
Language: en
Pages: 0
Authors: Clayton V. Deutsch
Categories: Petroleum
Type: BOOK - Published: 2023 - Publisher:

DOWNLOAD EBOOK

This title brings the practice of petroleum geostatistics into a coherent framework, focusing on tools, techniques, examples, and guidance. It emphasises intera
Seismic Reservoir Modeling
Language: en
Pages: 259
Authors: Dario Grana
Categories: Science
Type: BOOK - Published: 2021-05-04 - Publisher: John Wiley & Sons

DOWNLOAD EBOOK

Seismic reservoir characterization aims to build 3-dimensional models of rock and fluid properties, including elastic and petrophysical variables, to describe a
Geostatistical Methods for Reservoir Geophysics
Language: en
Pages: 159
Authors: Leonardo Azevedo
Categories: Science
Type: BOOK - Published: 2017-04-07 - Publisher: Springer

DOWNLOAD EBOOK

This book presents a geostatistical framework for data integration into subsurface Earth modeling. It offers extensive geostatistical background information, in