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Abstract Accurate prediction of relative permeability is essential for continuum‐scale simulations of multiphase flow in porous media. This study presents a workflow that couples a thermodynamic model with a data‐driven approach to estimate relative permeability directly from continuum‐scale wettability. By leveraging a pre‐trained neural network, the method bypasses the need for repetitive pore‐scale simulations and rapidly predicts permeability based on fluid configurations. Validation using CT images of a glass bead pack confirms the accuracy and physical consistency of the predictions. Integration with the MATLAB Reservoir Simulation Toolbox framework demonstrates the workflow's scalability and ease of application. This approach offers an innovative and efficient solution for modeling complex multiphase flow, advancing the computational tools available for large‐scale porous media simulations.
Ebadi et al. (Sat,) studied this question.