Computational Models For Co2 Geo Sequestration Compressed Air Energy Storage
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Computational Models for CO2 Geo-sequestration & Compressed Air Energy Storage
A comprehensive mathematical and computational modeling of CO2 Geosequestration and Compressed Air Energy StorageEnergy and environment are two interrelated issues of great concern to modern civilization. As the world population will soon reach eight billion, the demand for energy will dramatically increase, intensifying the use of fossil fuels. Ut
Computational Models for CO2 Geo-sequestration & Compressed Air Energy Storage
"This book addresses two distinct, but related and highly important geoenvironmental applications: CO2 sequestration in underground formation, and Compressed Air Energy Storage (CAES). Sequestration of carbon dioxide in underground formations is considered an effective technique and a viable strategy for the mitigation of global warming and climate change. However, the short-term and long-term consequences of such an operation might be catastrophic if the involved hydro-chemo-physical and mechanical processes at the regional level are not properly addressed. Compressed air energy storage is a relatively new field of geoenvironmental application, but gaining a lot of momentum due to its effective utilization for energy storage. Renewable energy sources, such as wind energy, can be efficiently stored in the form of a compressed air in underground formations at off-peak times, and re-utilized upon demand. However, pumping and releasing compressed air can cause hydromechanical effects on the region, causing subsidence, upheaving and minor earthquakes. Considering the great potentials of these two geoenvironmental applications and their consequences, it is vital to model the involved flow processes and develop numerical tools, which are capable of describing such processes in an accurate, stable and computationally efficient manner. This book aims at attaining such models and numerical tools."--
Data-Driven Analytics for the Geological Storage of CO2
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.