Data-Driven Analytics for the Geological Storage of CO2
Auteur : Mohaghegh Shahab
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.
Storage of CO2 in Geological Formations. Petroleum Data Analytics. Smart Proxy Modeling. CO2 Storage in Depleted Gas Reservoirs. CO2 Storage in Saline Aquifers. CO2 Storage in Shale using Smart Proxy. CO2 – EOR as a Storage Mechanism. Leak Detection in CO2 Storage Sites.
Shahab D. Mohaghegh, a pioneer in the application of Artificial Intelligence and Data Mining in the Exploration and Production industry, is the president and CEO of Intelligent Solutions, Inc. (ISI) and Professor of Petroleum and Natural Gas Engineering at West Virginia University. He holds B.S., MS, and PhD degrees in petroleum and natural gas engineering.
He has authored more than 150 technical papers and carried out more than 50 projects for NOCs and IOCs. He is a SPE Distinguished Lecturer and has been featured in the Distinguished Author Series of SPE’s Journal of Petroleum Technology (JPT) four times. He is the founder of Petroleum Data-Driven Analytics, SPE’s Technical Section dedicated to data mining. He has been honoured by the U.S. Secretary of Energy for his technical contribution in the aftermath of the Deepwater Horizon (Macondo) incident in the Gulf of Mexico and was a member of the U.S. Secretary of Energy’s Technical Advisory Committee on Unconventional Resources (2008–2014). He represents the United States in the International Standard Organization (ISO) on Carbon Capture and Storage.
Date de parution : 12-2020
15.6x23.4 cm
Date de parution : 05-2018
15.6x23.4 cm
Thèmes de Data-Driven Analytics for the Geological Storage of CO2 :
Mots-clés :
Reservoir Simulation Models; Proxy Model; Big data analytics for carbon dioxide storage; Reservoir Simulation; carbon storage; Grid Blocks; greenhouse gases; Si Conversion; carbon storage in saline aquifers; Spatio Temporal Database; Petroleum Data-Driven Analytics; History Matching; carbon capture and storage; Gas Saturation; CCS; Reservoir Pressure; Shahab D; Mohaghegh; Mole Fraction Distribution; Alireza Haghighat; Numerical Simulation Model; Shohreh Amini; Geological Storage; Amirmasoud Kalantari-Dahaghi; Blind Run; Alireza Shahkarami; Water Saturation Distribution; Vida Gholami; Depleted Gas Reservoir; Leakage Location; Calibration Dataset; Residual Gas Saturation; Leakage Rate; Injection Starts; Gas Relative Permeability; Hidden Neurons; Hydraulic Fracture; CCS Project; Bubble Point Pressure