Integration & Co-development of a Geophysical CO2 Monitoring Suite

Abstract

Carbon capture and sequestration (CCS) has emerged as a key technology for dramatic short-term reduction in greenhouse gas emissions in particular from large stationary. A key challenge in this arena is the monitoring and verification (M&V) of CO2 plumes in the deep subsurface. Towards that end, we have developed a tool that can simultaneously invert multiple sub-surface data sets to constrain the location, geometry, and saturation of subsurface CO2 plumes. We have focused on a suite of unconventional geophysical approaches that measure changes in electrical properties (electrical resistance tomography, electromagnetic induction tomography) and bulk crustal deformation (tilmeters). We had also used constraints of the geology as rendered in a shared earth model (ShEM) and of the injection (e.g., total injected CO 2 ).We describe a stochastic inversion method for mapping subsurface regions where CO 2 saturation is changing. The technique combines prior information with measurements of injected CO 2 volume, reservoir deformation and electrical resistivity. Bayesian inference and a Metropolis simulation algorithm form the basis for this approach. The method can a) jointly reconstruct disparate data types such as surface or subsurface tilt, electrical resistivity, and injected CO 2 volume measurements, b) provide quantitative measures of the result uncertainty, c) identify competing models when the available data are insufficient to definitively identify a single optimal model and d) rank the alternative models based on how well they fit available data.We present results from general simulations of a hypothetical case derived from a real site. We also apply the technique to a field in Wyoming, where measurements collected during CO 2 injection for enhanced oil recovery serve to illustrate the method's performance. The stochastic inversions provide estimates of the most probable location, shape, volume of the plume and most likely CO 2 saturation. The results suggest that the method can reconstruct data with poor signal to noise ratio and use hard constraints available from many sites and applications. External interest in the approach and method is high, and already commercial and DOE entities have requested technical work using the newly developed methodology for CO 2 monitoring.-2-