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Data-Driven Analytics for the Geological Storage of CO2

Author: Shahab Mohaghegh
Publisher: Boca Raton, FL : CRC Press, 2018.
Edition/Format:   eBook : Document : English : First editionView all editions and formats
Summary:
"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  Read more...
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Genre/Form: Electronic books
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Shahab Mohaghegh
ISBN: 9781315280813 1315280817 9781315280806 1315280809 9781315280790 1315280795 1138197149 9781138197145
OCLC Number: 1031316331
Description: 1 online resource : text file, PDF
Contents: Cover; Halftitle Page; Title Page; Copyright Page; Dedication; Contents; Nomenclature; Acknowledgments; Author; Contributors; Introduction; 1. Storage of CO2 in Geological Formations; 2. Petroleum Data Analytics; 3. Smart Proxy Modeling; 4. CO2 Storage in Depleted Gas Reservoirs; 5. CO2 Storage in Saline Aquifers; 6. CO2 Storage in Shale Using Smart Proxy; 7. CO2-EOR as a Storage Mechanism; 8. Leak Detection in CO2 Storage Sites; Bibliography; Index.
Responsibility: Shahab 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."--Provided by publisher.

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