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

Efendiev, Yalchin¹  Datta-Gupta, Akhil¹  Jafarpour, Behnam¹  Mallick, Bani¹  Vassilevski, Panayot¹ 

[1] Texas A & M Univ., College Station, TX (United States)

关键词: subsurface flow;    transport;    porous media;    heterogeneous;    Bayesian;    prior modeling;    posterior;    Markov chain Monte Carlo;    multiscale;   

DOI  :  10.2172/1225386
RP-ID  :  DOE-TAMU--SC0004965.
PID  :  OSTI ID: 1225386
Others  :  Other: 19795754068

学科分类：数学（综合）

美国|英语

来源: SciTech Connect

【 摘 要 】

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.