KAUST-IAMCS Workshop on Multiscale Modeling, Advanced Discretization Techniques, and Simulation of Wave Propagation
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- King Abdullah University of Science and Technology (KAUST)
- Thuwal, Saudi Arabia
- Jia Wei, Texas A&M University
- Bayesian Inversion for Channelized Reservoirs via Reduced Dimensional Parameterization
Abstract
We study the uncertainty quantification for flows in heterogeneous porous media. The permeability field is assumed to have channelized structure that are represented using level sets approaches. In particular, the parameterization of the channel boundaries is represented via the parameterization of velocity field in the level sets equations. The truncation in the parameter space introduces errors in the posterior measure that are investigated. Multi-stage MCMC algorithms are used for efficient sampling from the posterior. We present numerical results.