Shahab D. Mohaghegh

Shale Analytics (eBook, PDF)

Data-Driven Analytics in Unconventional Resources

• Format: PDF

Produktbeschreibung

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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Produktdetails

• Verlag: Springer Nature Switzerland
• Seitenzahl: 287
• Erscheinungstermin: 9. Februar 2017
• Englisch
• ISBN-13: 9783319487533
• Artikelnr.: 53031733

Autorenporträt

Shahab D. Mohaghegh is the president and CEO of Intelligent Solutions, Inc. (ISI) and Professor of Petroleum and Natural Gas Engineering at West Virginia University. A pioneer in the application of Artificial Intelligence and Data Mining in the Exploration and Production industry, he holds B.S., MS, and PhD degrees in petroleum and natural gas engineering. He has authored more than 180 technical papers and carried out more than 50 projects with major international companies. He is a SPE Distinguished Lecturer and has been featured in the Distinguished Author Series of SPE's Journal of Petroleum Technology (JPT) four times. He has been honored by the U.S. Secretary of Energy for his technical contribution in the aftermath of the Deepwater Horizon (Macondo) incident in the Gulf of Mexico and has served as a member of U.S. Secretary of Energy's Technical Advisory Committee on Unconventional Resources.

Inhaltsangabe

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.