Accelerating the Estimation of Effective Rock Properties Under Partially Saturated Conditions Through Saturation‐Conditioned U‐Net‐Based Initialization

Abstract

Effective properties describe transport processes in porous media, controlled by their geometric characteristics (i.e., pore‐size distribution, connectivity, and porosity) and phase configuration. Estimating effective properties of partially saturated porous media requires performing computationally expensive pore‐scale simulations. This process typically involves two sequential simulations that are high dimensional and require long simulation times. To address this challenge, machine learning methods are combined with physics‐based simulations to enable fast initialization. We propose the saturation‐conditioned U‐Net method for efficiently predicting initial phase configuration in pore‐network structures. This approach employs a shallow neural network to condition the U‐Net model via skip connections. We demonstrate the performance of this method using two pore‐network structures under different water saturation conditions. The method successfully delivers accurate predictions while maintaining reasonable computational resources requirement. It achieves nearly times speed‐up compared to Lattice‐Boltzmann simulations for predicting gas phase distribution in a 3D structure. When coupled with a physics‐based solver, this approach enables efficient and accurate calculation of effective properties of partially saturated media.