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.
Highlights
Geological carbon capture and storage (CCS) of CO2 is considered as one of the most practical solutions to help meet the Paris agreement target on greenhouse gas emissions
The approach presented here includes geostatistical methods for providing petrophysical properties, reservoir simulation, and rock physics models relating the CO2 in the subsurface to the seismic response variables ac quired at different monitoring times
The seismic monitoring data are available at many cells, the responses are quite correlated and might not carry as much information as one might expect
Summary
Geological carbon capture and storage (CCS) of CO2 is considered as one of the most practical solutions to help meet the Paris agreement target on greenhouse gas emissions. Building on a large number of simulation results, we use statistical learning methods to develop approaches for assessing the probabilities of early detection of leakage through a key fault at Smeaheia. This is done by computing the accuracy of characterizing the reservoir as leaking or sealing, given the simulated seismic data. Trainor-Guitton et al (2013) proposed a more nuanced VOI analysis methodology which consists of using nu merical modeling of electrical resistivity data to detect CO2/brine leakage Other studies addressing this topic are Harbert et al (2016) and Yang et al (2018). Our approach in the current paper is different from previous work in its focus on leakage monitoring and in
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