Joint reconstructions of CO2 plumes using a Markov Chain Monte Carlo approach

We describe a stochastic inversion method for mapping subsurface regions where CO{sub 2} saturation is changing. The technique combines prior information with measurements of injected CO{sub 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{sub 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 use measurements collected during CO{sub 2} injection for enhanced oil recovery 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{sub 2} saturation. The results suggest that the method can reconstruct data with poor signal to noise ratio.