Efficient sampling techniques for uncertainty quantification in history matching using nonlinear error models and ensemble level upscaling techniques

Y. Efendiev, A. Datta-Gupta, X. Ma, B. Mallick

Research output: Contribution to journalArticlepeer-review

19 Scopus citations


The Markov chain Monte Carlo (MCMC) is a rigorous sampling method to quantify uncertainty in subsurface characterization. However, the MCMC usually requires many flow and transport simulations in evaluating the posterior distribution and can be computationally expensive for fine-scale geological models. We propose a methodology that combines coarse- and fine-scale information to improve the efficiency of MCMC methods. The proposed method employs off-line computations for modeling the relation between coarse- and fine-scale error responses. This relation is modeled using nonlinear functions with prescribed error precisions which are used in efficient sampling within the MCMC framework. We propose a two-stage MCMC where inexpensive coarse-scale simulations are performed to determine whether or not to run the fine-scale (resolved) simulations. The latter is determined on the basis of a statistical model developed off line. The proposed method is an extension of the approaches considered earlier where linear relations are used for modeling the response between coarse-scale and fine-scale models. The approach considered here does not rely on the proximity of approximate and resolved models and can employ much coarser and more inexpensive models to guide the fine-scale simulations. Numerical results for three-phase flow and transport demonstrate the advantages, efficiency, and utility of the method for uncertainty assessment in the history matching. Copyright 2009 by the American Geophysical Union.
Original languageEnglish (US)
JournalWater Resources Research
Issue number11
StatePublished - Nov 13 2009
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2020-04-23
Acknowledged KAUST grant number(s): KUS-C1-016-04
Acknowledgements: We would like to acknowledge partial support from NSF grant CMG 0724704. This work is partly supported by award KUS-C1-016-04, made by King Abdullah University of Science and Technology (KAUST). We thank the reviewers for their comments that helped to improve the paper.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.


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