Assessing the value of seismic monitoring of CO2 storage using simulations and statistical analysis

Abstract Successful storage of CO2 in underground aquifers requires robust monitoring schemes for detecting potential leakage. To aid in this challenge we propose to use statistical approaches to gauge the value of seismic monitoring schemes in decision support systems. The new framework is based on geostatistical uncertainty modeling, reservoir simulations of the CO2 plume in the aquifer, and the associated synthetic seismic response for both leak and seal scenarios. From a large set of simulations we assess the leak and seal conditional probabilities given seismic data over time, and build on this to compute the value of information of the seismic monitoring schemes. The Smeaheia aquifer west of Norway is used to exemplify the approach for early leakage detection and decision support regarding CO2 storage projects. For this case study, we find that the optimal monitoring time is about 10 years after injection starts.

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