#rock-fluid-interaction News & Analysis

Surrogate models for Rock-Fluid Interaction: A Grid-Size-Invariant Approach

Researchers develop grid-size-invariant neural network surrogate models for predicting rock-fluid interactions in porous media, offering a computationally cheaper alternative to traditional high-fidelity simulations. The approach demonstrates that UNet++ architecture outperforms standard UNet for this application, enabling significant memory reduction during training while maintaining prediction accuracy.