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Data-driven analytics for the geological storage of CO2 / by Shahab Mohaghegh.

By: Mohaghegh, Shahab D [author.]Contributor(s): Taylor and FrancisMaterial type: Text

TextPublisher: Boca Raton, FL : CRC Press, an imprint of Taylor and Francis, 2018Description: 1 online resource (302 pages) : 226 illustrationsISBN: 9781315280813Subject(s): TECHNOLOGY & ENGINEERING / Chemical & Biochemical | TECHNOLOGY & ENGINEERING / Environmental / General | Geological carbon sequestration | BUSINESS & ECONOMICS / Infrastructure | SOCIAL SCIENCE / GeneralAdditional physical formats: Print version: : No titleDDC classification: 363.7387 LOC classification: TD885.5.C3Online resources: Click here to view.

Contents:

chapter 1 Storage of CO2 in Geological Formations / Shahab D. Mohaghegh -- chapter 2 Petroleum Data Analytics / Shahab D. Mohaghegh -- chapter 3 Smart Proxy Modeling / Shahab D. Mohaghegh -- chapter 4 CO2 Storage in Depleted Gas Reservoirs / Shahab D. Mohaghegh -- chapter 5 CO2 Storage in Saline Aquifers / Shahab D. Mohaghegh -- chapter 6 CO2 Storage in Shale Using Smart Proxy / Shahab D. Mohaghegh -- chapter 7 CO2-EOR as a Storage Mechanism / Shahab D. Mohaghegh -- chapter 8 Leak Detection in CO2 Storage Sites / Shahab D. Mohaghegh.

Abstract: 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.

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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.