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Shale Analytics
eBook
eBook
Fachbuch
2017

Shale Analytics

Data-Driven Analytics in Unconventional Resources

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ISBN
EAN
978-3-319-48753-3
9783319487533
Artikel-Nr.
9RY5945
Rabatt
-21.7
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CHF 141.50
CHF
110.73
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1
speech-bubble-svg Beschreibung

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.

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Stichwörter
Inhaltsverzeichnis
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.
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Autor/-in
Biografie

Shahab D. Mohaghegh, a pioneer in the application of Artificial Intelligence and Machine Learning in the Exploration and Production industry, is Professor of Petroleum and Natural Gas Engineering at West Virginia University (WVU) and the president and CEO of Intelligent Solutions, Inc. (ISI). He is the director of WVU-LEADS (Laboratory for Engineering Application of Data Science).

Including more than 30 years of research and development in the petroleum engineering application of Artificial Intelligence and Machine Learning, he has authored three books (Shale Analytics - Data Driven Reservoir Modeling - Application of Data-Driven Analytics for the Geological Storage of CO2), more than 230 technical papers and carried out more than 60 projects for independents, NOCs and IOCs. He is a SPE Distinguished Lecturer (2007 and 2020) and has been featured four times as the Distinguished Author in SPE's Journal of Petroleum Technology (JPT 2000 and 2005). He is the founder of SPE's Technical Section dedicated to AI and machine learning (Petroleum Data-Driven Analytics, 2011). He has been honored by the U.S. Secretary of Energy for his AI-based technical contribution in the aftermath of the Deepwater Horizon (Macondo) incident in the Gulf of Mexico (2011) and was a member of U.S. Secretary of Energy's Technical Advisory Committee on Unconventional Resources in two administrations (2008-2014). He represented the United States in the International Standard Organization (ISO) on Carbon Capture and Storage technical committee (2014-2016).

Publikation
Schweiz
09.02.2017
Springer Verlag
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Sprache
Englisch
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eBook
PDF
287 Seiten
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