Data-Driven Analytics for the Geological Storage of CO2

Shahab Mohaghegh

June 5, 2018 by CRC Press
Reference - 282 Pages - 84 Color & 142 B/W Illustrations
ISBN 9781138197145 - CAT# K31243

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Features

  • Explains the technology that allows for uncertainty quantification and optimization of CO2 storage projects.
  • Offers actual case studies from Australia and the United States.
  • Covers numerical simulation and carbon storage in geological formations.
  • Provides a data-driven solution to leak protection of carbon storage products.
  • Discusses area of review and post-injection site care.
  • 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 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.