Estimation and uncertainty analysis of the CO₂ storage volume in the sleipner field via 4D reversible-jump markov-chain Monte Carlo

Many scientists have developed technology to store CO₂ in the subsurface and to monitor the storage conditions to comply with the requirements for zero detectable leakage and greenhouse gas control. The goal of this research is to propose a novel workflow to estimate the stored CO₂ volume and to quantify the uncertainty of the injected volume. We implemented geophysical stochastic inversion using the time-lapse 3D seismic volumes as inputs, which provides an indirect estimation of the velocity changes and the migration path of the injected gas content. When performing the inversion, we employed the reversible-jump approach and used the Sleipner time-lapse 3D seismic volumes to demonstrate the proposed workflow. The inversion result was validated via forward modeling and pseudo well log interpretation. We then built a structural geology model and populated porosity logs by performing 500 realizations for volumetric analysis. In a comparison of the measured volume of the injected gas via volumetric analysis results, the predicted subsurface CO₂ volume linearly increases in the same phase with the injection rate, and the volume estimation error is less than 17%.