Sensitivity Analysis of Fluid–Fluid Interfacial Area, Phase Saturation and Phase Connectivity on Relative Permeability Estimation Using Machine Learning Algorithms

Sanchay Mukherjee and Russell T. Johns ()
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Sanchay Mukherjee: John and Willie Leone Department of Energy and Mineral Engineering, The Pennsylvania State University, State College, PA 16801, USA
Russell T. Johns: John and Willie Leone Department of Energy and Mineral Engineering, The Pennsylvania State University, State College, PA 16801, USA

Energies, 2022, vol. 15, issue 16, 1-10

Abstract: Recent studies have shown that relative permeability can be modeled as a state function which is independent of flow direction and dependent upon phase saturation ( S ), phase connectivity ( X ), and fluid–fluid interfacial area ( A ). This study evaluates the impact of each of the three state parameters ( S , X , and A ) in the estimation of relative permeability. The relative importance of the three state parameters in four separate quadrants of S-X-A space was evaluated using a machine learning algorithm (out-of-bag predictor importance method). The results show that relative permeability is sensitive to all the three parameters, S , X , and A, with varying magnitudes in each of the four quadrants at a constant value of wettability. We observe that the wetting-phase relative permeability is most sensitive to saturation, while the non-wetting phase is most sensitive to phase connectivity. Although the least important, fluid–fluid interfacial area is still important to make the relative permeability a more exact state function.

Keywords: relative permeability; state function; saturation; phase connectivity; fluid–fluid interfacial area; sensitivity analysis; out-of-bag predictor importance (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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