Joint reconstructions of CO2 plumes using a Markov Chain Monte Carlo approach [electronic resource].
- Washington, D.C. : United States. Dept. of Energy, 2006. and Oak Ridge, Tenn. : Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy.
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- PDF-file: 8 pages; size: 0 Kbytes
- Additional Creators:
- Lawrence Berkeley National Laboratory, United States. Department of Energy, and United States. Department of Energy. Office of Scientific and Technical Information
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- Free-to-read Unrestricted online access
- We describe a stochastic inversion method for mapping subsurface regions where CO₂ saturation is changing. The technique combines prior information with measurements of injected CO₂ 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 CO₂ 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 CO₂ 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 CO₂ saturation. The results suggest that the method can reconstruct data with poor signal to noise ratio.
- Published through SciTech Connect., 04/07/2006., "ucrl-proc-220448", Presented at: 8th International Conference on Greenhouse Gas Control Technologies, Trondheim, Norway, Jun 19 - Jun 22, 2006., and Kirkendall, B; Foxall, W; Daily, W; Ramirez, A; Friedmann, S J; Aines, R; Dyer, K.
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