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Title: Bayesian Uncertainty Quantification in Predictions of Flows in Highly Heterogeneous Media and Its Applications to the CO2 Sequestration

In this proposal, we have worked on Bayesian uncertainty quantification for predictions of fows in highly heterogeneous media. The research in this proposal is broad and includes: prior modeling for heterogeneous permeability fields; effective parametrization of heterogeneous spatial priors; efficient ensemble- level solution techniques; efficient multiscale approximation techniques; study of the regularity of complex posterior distribution and the error estimates due to parameter reduction; efficient sampling techniques; applications to multi-phase ow and transport. We list our publications below and describe some of our main research activities. Our multi-disciplinary team includes experts from the areas of multiscale modeling, multilevel solvers, Bayesian statistics, spatial permeability modeling, and the application domain.
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  1. Texas A & M Univ., College Station, TX (United States)
Publication Date:
OSTI Identifier:
Report Number(s):
DOE Contract Number:
Resource Type:
Technical Report
Research Org:
Texas A & M Univ., College Station, TX (United States)
Sponsoring Org:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)
Country of Publication:
United States
97 MATHEMATICS AND COMPUTING subsurface flow; transport; porous media; heterogeneous; Bayesian; prior modeling; posterior; Markov chain Monte Carlo; multiscale