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Geostatistical methods for reservoir...

Azevedo, Leonardo.

Geostatistical methods for reservoir geophysics

Record Type:	Electronic resources : Monograph/item
Title/Author:	Geostatistical methods for reservoir geophysicsby Leonardo Azevedo, Amilcar Soares.
Author:	Azevedo, Leonardo.
other author:	Soares, Amilcar.
Published:	Cham :Springer International Publishing :2017.
Description:	xxvii, 141 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	PetroleumGeology
Online resource:	http://dx.doi.org/10.1007/978-3-319-53201-1
ISBN:	9783319532011$q(electronic bk.)

Geostatistical methods for reservoir geophysics
Azevedo, Leonardo.

Geostatistical methods for reservoir geophysics[electronic resource] /by Leonardo Azevedo, Amilcar Soares. - Cham :Springer International Publishing :2017. - xxvii, 141 p. :ill., digital ;24 cm. - Advances in oil and gas exploration & production,2509-372X. - Advances in oil and gas exploration & production..

Introduction -- Fundamental geostatistical tools for data integration -- Simulation Models of Physical Phenomena in Earth Sciences -- Integration of geophysical data for reservoir modeling and characterization -- Data integration into geostatistical seismic inversion methodologies -- Afterword.

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.

ISBN: 9783319532011$q(electronic bk.)

Standard No.: 10.1007/978-3-319-53201-1doiSubjects--Topical Terms:

709792
Petroleum
--Geology

LC Class. No.: TN870.53

Dewey Class. No.: 622.3382

Geostatistical methods for reservoir geophysics
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245 1 0 $a Geostatistical methods for reservoir geophysics $h [electronic resource] / $c by Leonardo Azevedo, Amilcar Soares.
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505 0 $a Introduction -- Fundamental geostatistical tools for data integration -- Simulation Models of Physical Phenomena in Earth Sciences -- Integration of geophysical data for reservoir modeling and characterization -- Data integration into geostatistical seismic inversion methodologies -- Afterword.
520 $a 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.
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