Uncertainty quantification of CO2 storage using Bayesian model averaging and polynomial chaos expansion

For the Texas case study, we developed a systematic approach to quantify overall uncertainty, including both model uncertainty and parameter uncertainty. The approach was applied to forecast results at three important time steps, the end of the 30-year CO2-EOR injection period, the end of the 20-year post EOR CO2 injection period, and the end of the 50-year monitoring period. Results suggest that oil solubility dominates CO2 trapping and aqueous solubility has the least relative importance with respect to trapping (storage). Predictions of model averaging preserved the general pattern and captured differences among alternative models. CO2 storage of the reference model was within one standard deviation of predictions of model averaging. Estimated relative error between forecasted CO2 storage and the reference model are 0.8%, 7.4%, and 6.1% at three selected time steps. By the end of simulation, estimated CO2 storage in five selected layers in oil, supercritical, and aqueous phases are 3.4 ± 0.3 million tonnes, 2.0 ± 0.25 million tonnes, and 0.24 ± 0.04 million tonnes, respectively.