Journal
INTERNATIONAL JOURNAL FOR UNCERTAINTY QUANTIFICATION
Volume 3, Issue 6, Pages 499-522Publisher
BEGELL HOUSE INC
DOI: 10.1615/Int.J.UncertaintyQuantification.2013005281
Keywords
Bayesian statistics; GPUs; Markov chain Monte Carlo; single-phase; two-phase; uncertainty quantification
Funding
- DOE [DE-FE0004832, DE-SC0004982]
- Center for Fundamentals of Subsurface Flow of the School of Energy Resources of the University of Wyoming [WYDEQ49811GNTG, WYDEQ49811PER, WYDEQ49811FRTD]
- Clean Coal Technologies Research Program of the School of Energy Resources of the University of Wyoming [1100 20352 2012]
- NSF [DMS-1016283]
- Direct For Mathematical & Physical Scien
- Division Of Mathematical Sciences [1016283] Funding Source: National Science Foundation
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We are concerned with the development of computationally efficient procedures for subsurface flow prediction that relies on the characterization of subsurface formations given static (measured permeability and porosity at well locations) and dynamic (measured produced fluid properties at well locations) data. We describe a predictive procedure in a Bayesian framework, which uses a single-phase flow model for characterization aiming at making prediction for a two-phase flow model. The quality of the characterization of the underlying formations is accessed through the prediction of future fluid flow production.
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