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.

chapter 1|6 pages

Storage of CO2 in Geological Formations

ByShahab D. Mohaghegh, Alireza Haghighat, Shohreh Amini

chapter 2|25 pages

Petroleum Data Analytics

ByShahab D. Mohaghegh

chapter 3|12 pages

Smart Proxy Modeling

ByShahab D. Mohaghegh

chapter 4|20 pages

CO2 Storage in Depleted Gas Reservoirs

ByShahab D. Mohaghegh, Shohreh Amini

chapter 5|54 pages

CO2 Storage in Saline Aquifers

ByShahab D. Mohaghegh, Alireza Haghighat

chapter 6|21 pages

CO2 Storage in Shale Using Smart Proxy

ByShahab D. Mohaghegh, Amirmasoud Kalantari-Dahaghi

chapter 7|40 pages

CO2-EOR as a Storage Mechanism

ByShahab D. Mohaghegh, Alireza Shahkarami, Vida Gholami

chapter 8|73 pages

Leak Detection in CO2 Storage Sites

ByShahab D. Mohaghegh, Alireza Haghighat