Record Display for the EPA National Library Catalog

RECORD NUMBER: 20 OF 88

OLS Field Name OLS Field Data
Main Title Data-driven analytics for the geological storage of CO2 /
Type EBOOK
Author Mohaghegh, Shahab D.,
Publisher CRC Press, an imprint of Taylor and Francis,
Year Published 2018
Call Number TD885.5.C3
ISBN 9781315280813
Subjects TECHNOLOGY & ENGINEERING / Chemical & Biochemical. ; TECHNOLOGY & ENGINEERING / Environmental / General. ; Geological carbon sequestration. ; BUSINESS & ECONOMICS / Infrastructure. ; SOCIAL SCIENCE / General.
Internet Access
Description Access URL
https://www.taylorfrancis.com/books/9781315280813
Collation 1 online resource (302 pages) : 226 illustrations
Notes
Due to license restrictions, this resource is available to EPA employees and authorized contractors only
Contents Notes
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.