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Adaptive approach to petroleum reser...

Ursegov, Stanislav.

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Adaptive approach to petroleum reservoir simulation

Record Type:	Electronic resources : Monograph/item
Title/Author:	Adaptive approach to petroleum reservoir simulation/ by Stanislav Ursegov, Armen Zakharian.
Author:	Ursegov, Stanislav.
other author:	Zakharian, Armen.
Published:	Cham :Springer International Publishing : : 2021.,
Description:	ix, 86 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	Introduction -- Value of information for reservoir simulation -- Contrasts between adaptive and deterministic models -- Alternatives for mathematical apparatus of adaptive simulation - neural networks and fuzzy logic -- Adaptive geological modeling -- Adaptive hydrodynamic modeling -- Adaptive forecasting -- Adaptive software system Cervart -- Conclusion.
Contained By:	Springer Nature eBook
Subject:	Petroleum - Prospecting -
Online resource:	https://doi.org/10.1007/978-3-030-67474-8
ISBN:	9783030674748

Adaptive approach to petroleum reservoir simulation
Ursegov, Stanislav.

Adaptive approach to petroleum reservoir simulation[electronic resource] /by Stanislav Ursegov, Armen Zakharian. - Cham :Springer International Publishing :2021. - ix, 86 p. :ill. (some col.), digital ;24 cm. - Advances in oil and gas exploration & production,2509-372X. - Advances in oil and gas exploration & production..

Introduction -- Value of information for reservoir simulation -- Contrasts between adaptive and deterministic models -- Alternatives for mathematical apparatus of adaptive simulation - neural networks and fuzzy logic -- Adaptive geological modeling -- Adaptive hydrodynamic modeling -- Adaptive forecasting -- Adaptive software system Cervart -- Conclusion.

This book presents unique features of the adaptive modeling approach based on new machine learning algorithms for petroleum exploration, development, and production. The adaptive approach helps simulation engineers and geoscientists to create adequate geological and hydrodynamic models. This approach is proven to be a real alternative to traditional techniques, such as deterministic modeling. Currently, machine-learning algorithms grow in popularity because they provide consistency, predictiveness, and convenience. The primary purpose of this book is to describe the theoretical state of the adaptive approach and show some examples of its implementation in simulation and forecasting different reservoir processes.

ISBN: 9783030674748

Standard No.: 10.1007/978-3-030-67474-8doiSubjects--Topical Terms:

604496
Petroleum
--Prospecting

LC Class. No.: TN271.P4 / U774 2021

Dewey Class. No.: 622.1828

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100 1 $a Ursegov, Stanislav. $3 3489597
245 1 0 $a Adaptive approach to petroleum reservoir simulation $h [electronic resource] / $c by Stanislav Ursegov, Armen Zakharian.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2021.
300 $a ix, 86 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Advances in oil and gas exploration & production, $x 2509-372X
505 0 $a Introduction -- Value of information for reservoir simulation -- Contrasts between adaptive and deterministic models -- Alternatives for mathematical apparatus of adaptive simulation - neural networks and fuzzy logic -- Adaptive geological modeling -- Adaptive hydrodynamic modeling -- Adaptive forecasting -- Adaptive software system Cervart -- Conclusion.
520 $a This book presents unique features of the adaptive modeling approach based on new machine learning algorithms for petroleum exploration, development, and production. The adaptive approach helps simulation engineers and geoscientists to create adequate geological and hydrodynamic models. This approach is proven to be a real alternative to traditional techniques, such as deterministic modeling. Currently, machine-learning algorithms grow in popularity because they provide consistency, predictiveness, and convenience. The primary purpose of this book is to describe the theoretical state of the adaptive approach and show some examples of its implementation in simulation and forecasting different reservoir processes.
650 0 $a Petroleum $x Prospecting $x Data processing. $3 604496
650 0 $a Oil fields $x Computer simulation. $3 815737
650 0 $a Machine learning. $3 533906
650 1 4 $a Geotechnical Engineering & Applied Earth Sciences. $3 1565915
650 2 4 $a Geoengineering, Foundations, Hydraulics. $3 1001783
650 2 4 $a Geophysics/Geodesy. $3 891016
700 1 $a Zakharian, Armen. $3 3489598
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Advances in oil and gas exploration & production. $3 3216539
856 4 0 $u https://doi.org/10.1007/978-3-030-67474-8
950 $a Earth and Environmental Science (SpringerNature-11646)