Data-Driven Analytics for the Geological Storage of CO2

Shahab Mohaghegh

ISBN 10: 1138197149 / ISBN 13: 9781138197145

Verlag: CRC Press, 2018

Beschreibung

nach der Bestellung gedruckt Neuware - Printed after ordering - Data-driven analytics is enjoying unprecedented popularity among oil and gas professionals. A large number of reservoir engineering problems associated with geological storage of CO2 require development of numerical reservoir simulation models. The numerical models are used to understand the impact of injection of CO2 in saline aquifers, depleted oil and gas reservoirs, as well as CO2-EOR projects. This book discusses application of data-driven analytics to geological storage of CO2. It explains the technology that allows for uncertainty quantification and optimization of CO2 storage projects. It also deals with actual case studies from Australia and the United States.

Bestandsnummer des Verkäufers 9781138197145

Bibliografische Details

• Titel: Data-Driven Analytics for the Geological Storage of CO2
• Verlag: CRC Press
• Erscheinungsjahr: 2018
• Sprache: Englisch
• ISBN-10: 1138197149
• ISBN-13: 9781138197145
• Einband: Buch
• Zustand: Neu

Über diesen Titel

Inhaltsangabe

Data-driven analytics is enjoying unprecedented popularity among oil and gas professionals. Many reservoir engineering problems associated with geological storage of CO₂ require the development of numerical reservoir simulation models. This book is the first to examine the contribution of artificial intelligence and machine learning in data-driven analytics of fluid flow in porous environments, including saline aquifers and depleted gas and oil reservoirs. Drawing from actual case studies, this book demonstrates how smart proxy models can be developed for complex numerical reservoir simulation models. Smart proxy incorporates pattern recognition capabilities of artificial intelligence and machine learning to build smart models that learn the intricacies of physical, mechanical and chemical interactions using precise numerical simulations. This ground breaking technology makes it possible and practical to use high fidelity, complex numerical reservoir simulation models in the design, analysis and optimization of carbon storage in geological formations projects.

Über die Autorin bzw. den Autor

Shahab D. Mohaghegh, a pioneer in the application of Artificial Intelligence and Data Mining in the Exploration and Production industry, is the president and CEO of Intelligent Solutions, Inc. (ISI) and Professor of Petroleum and Natural Gas Engineering at West Virginia University. He holds B.S., MS, and PhD degrees in petroleum and natural gas engineering.

He has authored more than 150 technical papers and carried out more than 50 projects for NOCs and IOCs. 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 is the founder of Petroleum Data-Driven Analytics, SPE’s Technical Section dedicated to data mining. He has been honoured 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 was a member of the U.S. Secretary of Energy’s Technical Advisory Committee on Unconventional Resources (2008–2014). He represents the United States in the International Standard Organization (ISO) on Carbon Capture and Storage.