Monitoring and reconstruction of subsurface CO2 plumes using a stochastic inversion approach

Ramirez, Abelardo L.¹, William Foxall¹, Kathy Dyer¹, S. Julio Friedmann¹ (1) Lawrence Livermore National Laboratory, Livermore, CA

We have developed and tested a stochastic inversion tool that uses multiple data types to reconstruct subsurface liquid plumes (e.g., CO₂, steam, water floods). The tool uses Bayesian inference, a probabilistic approach that combines observed data, geophysical forward models, and prior knowledge. It produces plume images that are consistent with disparate data types, e.g., measurements of injected plume CO₂ volume, surface tilt measurements, and cross-borehole electrical resistivity measurements. It uses a Markov Chain Monte Carlo (MCMC) technique to sample the space of possible plume models, including the shape, location and CO₂ content of the plume. We present joint reconstructions of injected CO₂ volume and cross-borehole electrical resistivity data collected during a real CO₂ flood in the Powder River Basin. We will also show joint reconstructions of synthetic reservoir models, surface tilt and cross-borehole electrical resistivity data. The results demonstrate the benefits of joint reconstructions using disparate data. They also demonstrate that our approach identifies alternative models when the available data is insufficient to definitively identify a single optimal model, and also provides the probability that a given model is the best explanation for the available data. This information can guide further data collection or integration. Furthermore, the method provides quantitative measures of the solution uncertainty due to unknown reservoir properties, measurement error, or poor sensitivity to the plume by the geophysical techniques. This work was funded by the Laboratory Directed Research and Development Program at Lawrence Livermore National Laboratory. This work was performed under the auspices of the U.S. Department of Energy by the Lawrence Livermore National Laboratory under contract W-7405-ENG-48.