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Adaptive approach to petroleum reser...

Ursegov, Stanislav.

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Adaptive approach to petroleum reservoir simulation

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Adaptive approach to petroleum reservoir simulation/ by Stanislav Ursegov, Armen Zakharian.
作者:	Ursegov, Stanislav.
其他作者:	Zakharian, Armen.
出版者:	Cham :Springer International Publishing : : 2021.,
面頁冊數:	ix, 86 p. :ill. (some col.), digital ;24 cm.
內容註:	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
標題:	Petroleum - Prospecting -
電子資源:	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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