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Shale analytics = data-driven analyt...

Mohaghegh, Shahab D.

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Shale analytics = data-driven analytics in unconventional resources /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Shale analytics/ by Shahab D. Mohaghegh.
Reminder of title:	data-driven analytics in unconventional resources /
Author:	Mohaghegh, Shahab D.
Published:	Cham :Springer International Publishing : : 2017.,
Description:	xiv, 287 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	Data-Driven Formation Evaluation - Generation of Synthetic Geo-mechanical Well Logs in Shale -- Data-Driven Reservoir Characteristics - Impact of rock and completion parameters in -- Data-Driven Completion Analysis - Analysis, Design and Optimization of Hydraulic Fracturing in Shale -- Data-Driven Reservoir Modeling - Full Field Reservoir Modeling of Marcellus Shale -- Data-Driven Reservoir Modeling - Full Field Reservoir Modeling of Niobrara Formation, DJ Basin -- Data-Driven Reservoir Modeling - AI-Based Proxy of Numerical Reservoir Simulation of Shale.
Contained By:	Springer eBooks
Subject:	Shale gas - Data processing. -
Online resource:	http://dx.doi.org/10.1007/978-3-319-48753-3
ISBN:	9783319487533

Shale analytics = data-driven analytics in unconventional resources /
Mohaghegh, Shahab D.

Shale analyticsdata-driven analytics in unconventional resources /[electronic resource] :by Shahab D. Mohaghegh. - Cham :Springer International Publishing :2017. - xiv, 287 p. :ill. (some col.), digital ;24 cm.

Data-Driven Formation Evaluation - Generation of Synthetic Geo-mechanical Well Logs in Shale -- Data-Driven Reservoir Characteristics - Impact of rock and completion parameters in -- Data-Driven Completion Analysis - Analysis, Design and Optimization of Hydraulic Fracturing in Shale -- Data-Driven Reservoir Modeling - Full Field Reservoir Modeling of Marcellus Shale -- Data-Driven Reservoir Modeling - Full Field Reservoir Modeling of Niobrara Formation, DJ Basin -- Data-Driven Reservoir Modeling - AI-Based Proxy of Numerical Reservoir Simulation of Shale.

This book describes the application of modern information technology to reservoir modeling and well management. Data Driven Analytics in Unconventional Resources looks specifically at reservoir modeling and production management of shale reservoirs, since conventional reservoir modeling techniques do not perform well in this environment. Topics covered include tools for analysis, predictive modeling and optimization of production from shale in the absence of well-understood and well-defined physics of fluid flow in shale. Also discussed are important insights into completion practices of production from shale Abundant examples and computer code are given that illustrate the operation of Data-Driven Analytics. The flexibility and power of the technique is demonstrated in numerous real-world situations.

ISBN: 9783319487533

Standard No.: 10.1007/978-3-319-48753-3doiSubjects--Topical Terms:

3221687
Shale gas
--Data processing.

LC Class. No.: TD195.G3

Dewey Class. No.: 553.283

Shale analytics = data-driven analytics in unconventional resources /
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245 1 0 $a Shale analytics $h [electronic resource] : $b data-driven analytics in unconventional resources / $c by Shahab D. Mohaghegh.
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300 $a xiv, 287 p. : $b ill. (some col.), digital ; $c 24 cm.
505 0 $a Data-Driven Formation Evaluation - Generation of Synthetic Geo-mechanical Well Logs in Shale -- Data-Driven Reservoir Characteristics - Impact of rock and completion parameters in -- Data-Driven Completion Analysis - Analysis, Design and Optimization of Hydraulic Fracturing in Shale -- Data-Driven Reservoir Modeling - Full Field Reservoir Modeling of Marcellus Shale -- Data-Driven Reservoir Modeling - Full Field Reservoir Modeling of Niobrara Formation, DJ Basin -- Data-Driven Reservoir Modeling - AI-Based Proxy of Numerical Reservoir Simulation of Shale.
520 $a This book describes the application of modern information technology to reservoir modeling and well management. Data Driven Analytics in Unconventional Resources looks specifically at reservoir modeling and production management of shale reservoirs, since conventional reservoir modeling techniques do not perform well in this environment. Topics covered include tools for analysis, predictive modeling and optimization of production from shale in the absence of well-understood and well-defined physics of fluid flow in shale. Also discussed are important insights into completion practices of production from shale Abundant examples and computer code are given that illustrate the operation of Data-Driven Analytics. The flexibility and power of the technique is demonstrated in numerous real-world situations.
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