Assessing the value of seismic monitoring of CO2 storage using simulations and statistical analysis. (February 2021) - Anyosa, Susan; Bunting, Scott; Eidsvik, Jo; Romdhane, Anouar; Bergmo, Per
Highlights: Provide a new approach for evaluating monitoring schemes of CO2 injections. Use the Smeaheia region in Norway as a study case, performing the simulation of reservoir properties and generating synthetic seismic data. Fit statistical learning models using the seismic features to predict probabilities of leaking or sealing fault in aquifer. Conduct value of information analysis for a decision situation of whether to continue CO2 injection or stop. Compare results of seismic data processing methods and possible times of monitoring. 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.