| In an effort to reduce the amounts of anthropogenic CO 2 in the atmosphere, numerous carbon capture and storage (CCS) technologies are currently under intense development worldwide. Sequestration processes are very complex, and there is a great deal of risk and uncertainty surrounding CCS technologies and related policies. The purpose of this work is to model risk and uncertainty in this context, and develop and demonstrate numerical algorithms to solve these models. A computationally efficient framework for risk assessment of CO2 geologic sequestration has been developed. Tools from Monte Carlo (MC) simulation, polynomial chaos expansion (PCE), derivative-free optimization (DFO), stochastic programming, global optimization, and machine learning are utilized for this purpose in combination with models for flow through porous media. The methodology developed can be used to provide useful insights for policy makers and industrial companies. |
