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• W1900850489 abstract "This study develops and evaluates an integrated methodology including parameter zonation, efficient global optimization, and multiple aquifer realizations that gives a useful forecast of the pressure distribution and the CO 2 plume migration through a heterogeneous storage formation, over time, using only a few monitoring locations in a feasibly short amount of computing time, while dealing with data error. Geological characteristics of the example application are similar to a CO 2 sequestration pilot test conducted in a fluvial‐deltaic geological setting. In the example, the CO 2 injection problem is simulated with the TOUGH2 code, a numerical simulator for multiphase (gas and aqueous), multicomponent flow and transport. The inverse problem is difficult because the GCS optimization estimation problem has multiple local minima and the simulation is computationally expensive. The efficient surrogate response surface global optimization algorithm “Stochastic RBF” (stochastic radial basis function) is used to calibrate the model parameters. Results are averaged over three saline aquifer realizations. In the third numerical experiment using only pressure data, the CO 2 plume could be determined with an average correlation coefficient compared to the actual plume of up to R 2 = 0.916 (for current plume at t = 1.5 years) and average R 2 = 0.80 (for forecasted plume estimated at t = 7.5 years years, using only the first 1.5 years of monitoring data). Adding gas saturation data improved the 6 year forecast somewhat (increasing average R 2 = 0.85) but it was not significantly helpful in estimating the current plume. Both our inverse methodology and findings can be broadly applicable to GCS in heterogeneous sedimentary formations." @default.
• W1900850489 created "2016-06-24" @default.
• W1900850489 creator A5026808870 @default.
• W1900850489 creator A5044728534 @default.
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• W1900850489 date "2013-07-01" @default.
• W1900850489 modified "2023-10-18" @default.
• W1900850489 title "Estimation of plume distribution for carbon sequestration using parameter estimation with limited monitoring data" @default.
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• W1900850489 doi "https://doi.org/10.1002/wrcr.20326" @default.
• W1900850489 hasPublicationYear "2013" @default.
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