Probabilistic Risk Assessment for CO2 Storage Scenarios via Massive Stochastic Model Reduction

Large-scale industrial CO2 injection into deep geologic formations bears an inherent risk of leakage. The potential of CO2 injection as climate change mitigation solution will vastly depend on our ability to quantify the uncertainties and risk of the technology. Up to date, field experience is limited and no probabilistic risk assessment has been applied. Current numerical models are inadequate for stochastic simulation techniques, because they are computationally too expensive.

The goal of this project is to create a stochastic approach for a probabilistic risk assessment of CO2 storage scenarios. Our approach is based on a model reduction via polynomial response surfaces. After an initial computational effort for model reduction, the reduced polynomial model is vastly faster than the original model. A probabilistic risk assessment can then be performed within a short time.

The reduced model will aid in follow-up tasks, such as the optimization of site exploration campaigns. Due to the high solving speed after the initial model reduction, this tool can also be used for interactive tasks, such as design and real time control of the injection sites.