Advanced simulation of CO2 storage in saline aquifers
Machine learning and multiscale approach
M. Zhao (TU Delft - Aerodynamics)
M.I. Gerritsma – Promotor (TU Delft - Aerodynamics)
H. Hajibeygi – Promotor (TU Delft - Reservoir Engineering)
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Abstract
The secure and efficient storage of carbon dioxide (CO2) in deep saline aquifers is widely recognized as a critical component in the global strategy for mitigating anthropogenic climate change. Accurate and computationally efficient modeling of CO2 migration, phase behavior, and long-term trapping mechanisms in geologically heterogeneous formations remains an open and pressing challenge. This dissertation aims to advance modeling techniques for multiphase multicomponent flow in porous media, with a focus on two complementary directions: physics-constrained deep learning and multiscale numerical solvers. Both approaches seek to provide computational efficiency while delivering accurate results on a desired level, thereby enabling predictive simulations across a range of spatial and temporal scales.