|
Activity Number:
|
62
|
|
Type:
|
Topic Contributed
|
|
Date/Time:
|
Sunday, August 2, 2009 : 4:00 PM to 5:50 PM
|
|
Sponsor:
|
Section on Bayesian Statistical Science
|
| Abstract - #304013 |
|
Title:
|
Bayesian Uncertainty Quantification for Flows in Heterogeneous Subsurfaces Using Multiscale, Multidimensional Models
|
|
Author(s):
|
Anirban Mondal*+ and Bani K. Mallick and Yalchin Efendiev and Akhil Datta-Gupta
|
|
Companies:
|
Texas A&M University and Texas A&M University and Texas A&M University and Texas A&M University
|
|
Address:
|
Department of Statistics, College Station, TX, 77843,
|
|
Keywords:
|
Reversible jump Markov chain Monte Carlo ; Multiscale finite element methods ; Karhunen-Loeve expansion
|
|
Abstract:
|
In this paper, we present reversible jump Markov chain Monte Carlo algorithms for hierarchical modeling of channelized permeability fields and their use in uncertainty quantification in inverse problems. Within each channel, the permeability is assumed to have a log-normal distribution. Uncertainty quantification in history matching is carried out hierarchically by constructing facies boundaries as well as permeability fields within each facies. The search with Metropolis-Hastings algorithm results to low acceptance rate, and consequently, the computations are CPU demanding. To speed-up the computations, we use up-scaled models to screen the proposals. Our computations show that the proposed algorithms are capable of capturing the channel boundaries and provide accurate predictions of subsurface properties.
|
