Stochastic inverse modeling of transient laboratory-scale three-dimensional two-phase core flooding scenarios

We develop a comprehensive and efficient workflow for stochastic assessment of key parameters governing two-phase flow conditions associated with core-scale experiments. rely on original detailed datasets collected Berea sandstone sample. These capture the temporal evolution pressure drop across core three-dimensional maps phase saturations (determined via X-ray CT) in oil- brine-displacement flooding scenarios characterized by diverse brine/oil viscosity contrasts. Such experiments are used as test-bed proposed model calibration strategy. The latter is structured three main steps: (i) preliminary calibration, aimed at identifying behavioral region parameter space; (ii) Global Sensitivity Analysis (GSA), geared towards identification relative importance observed outputs non-influential to reduce dimensionality (iii) inverse modeling procedure. based differential-evolution genetic algorithm efficiently explore reduced space stemming from GSA. It enables one obtain probabilistic description relevant through their frequency distributions conditional type information collected. Coupling GSA estimation approach we consider streamlining procedure effectively cope considerable computational efforts linked scenario considered. Results show remarkable agreement experimental data imbue us confidence potential embed rich considered fully including uncertainty.