Shale Analytics: Data-Driven Analytics in Unconventional Resources

Mohaghegh, Shahab D

ISBN 10: 3319487515 / ISBN 13: 9783319487519

Editore: Springer/Sci-Tech/Trade, 2017

Riguardo questo articolo

Num Pages: 287 pages, 8 black & white illustrations, 235 colour illustrations, 15 colour tables, biography. BIC Classification: RBGL; THF; UNF; UYQ. Category: (P) Professional & Vocational. Dimension: 235 x 155 x 18. Weight in Grams: 619. . 2017. 1st ed. 2017. hardcover. . . . . Books ship from the US and Ireland.

Codice articolo V9783319487519

Dati bibliografici

• Titolo: Shale Analytics: Data-Driven Analytics in Unconventional Resources
• Editore: Springer/Sci-Tech/Trade
• Data di pubblicazione: 2017
• Lingua: Inglese
• ISBN 10: 3319487515
• ISBN 13: 9783319487519
• Rilegatura: Rilegato
• Condizione: New

Descrizione articolo

Riassunto

This book describes the application of modern information technology to reservoir modeling and well management in shale. While covering Shale Analytics, it focuses on reservoir modeling and production management of shale plays, since conventional reservoir and production 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 presence of massive multi-cluster, multi-stage hydraulic fractures. Given the fact that the physics of storage and fluid flow in shale are not well-understood and well-defined, Shale Analytics avoids making simplifying assumptions and concentrates on facts (Hard Data - Field Measurements) to reach conclusions. Also discussed are important insights into understanding completion practices and re-frac candidate selection and design. The flexibility and power of the technique is demonstrated in numerous real-world situations.

Informazioni sull?autore

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.