CO2 Leakage Risk Mitigation Using Reservoir Simulation and Optimization Algorithms

Carbon Capture and Storage (CCS) is being used in different countries around the world as an alternative to reduce CO2 emissions into the atmosphere. One way to reduce CO2 emissions is by storing CO2 in saline aquifers. However, many risks are involved during these operations such as CO2 containment, caprock leakage and fault reactivation.

To evaluate the feasibility of implementing a CCS project, it is necessary to consider different physical/chemical effects and operational parameters that would affect the risk of CO2 leakage such as the trapping mechanism (e.g., structural trapping, residual gas or mineralization, among others), injection volumes and rates, which can be quantified using numerical simulation of reservoirs. All these parameters need to be evaluated simultaneously to decrease uncertainty, making it a challenging evaluation.

In this study, a compositional/geomechanical/geochemistry coupled reservoir simulator is used to implement an optimization methodology using artificial intelligence that allows determining the optimal volume of CO2 and the injection strategy that should be implemented to storage CO2 while avoiding CO2 leakage to the surface.