The emissions of CO2 are an environmental problem and one possible solution is its capture and conduct underground storage (CSS). However, there is potential risk of leakage, and to aid in this challenge we propose to use statistical modeling for efficient monitoring and classification of sealing and leakage scenarios in the Smeaheia aquifer, in Norway. In this work, the approach is based on geostatistical simulations of the CO2 plume in the aquifer and on generating synthetic seismic data, for both leak and seal scenarios. The knowledge of estimated leakage probabilities allows better monitoring schemes and early leakage detection over time, which can be designed to support the decision making process on CO2 CSS projects.


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