Objective/Scopes: We aim at developing a viable workflow for the characterization of reservoir responsesunder Water Alternating Gas (WAG) conditions for enhanced oil recovery. We do so through a numericalMonte Carlo (MC) framework and by relying on (i) a classical approach, which is grounded on employingresults from laboratory-scale core-flooding experiments or (ii) an approach based on relative permeabilitycurves inferred from pore-scale numerical simulations. In these settings we investigate (i) the wayuncertainties associated with the parameters of a reservoir model estimated through these approachespropagate to target modeling goals and (ii) assess (through Global Sensitivity Analyses) the relativeimportance of the uncertain quantities controlling the reservoir behavior via given model outcomes. Methods/Procedures: We consider uncertainty in (a) porosity and absolute permeability as well as (b)parameters of relative permeability models. Three scenarios are assessed, accounting for spatial distributionof porosity and absolute permeability with differing degrees of complexity and corresponding to (i)homogeneous; (ii) randomly heterogeneous; and (iii) well-connected randomly heterogeneous fields. Spatialrealizations of the heterogeneous fields are generated considering Gaussian random fields with a Gaussiankernel variance driving the degree of spatial correlation. The two modeling approaches considered takeadvantage of two-phase relative permeability curves, which are interpreted via commonly used modelswith uncertain parameters. Three-phase relative permeabilities are then characterized through a previouslydeveloped and tested sigmoid-based oil relative permeability model by taking into account hystereticbehavior of gas relative permeability. All field-scale simulations are performed on a simple reservoir modeland are set within the MRST suite.
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