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A Multi-Stage Bayesian Prediction Framework for Subsurface Flows

Abstract. 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 characterization of the underlying formations is accessed through the prediction of future fluid flow production.