FFINO: Factorized Fourier Improved Neural Operator for Modeling Multiphase Flow in Underground Hydrogen Storage
This work addresses the need for efficient surrogate models in underground hydrogen storage management, offering a domain-specific incremental improvement over existing neural operators.
The authors tackled fast modeling of hydrogen plume migration and pressure field evolution in underground hydrogen storage by proposing FFINO, a neural operator architecture that reduces trainable parameters by 38.1%, training time by 17.6%, and achieves 9.8% accuracy improvement in predictions compared to the state-of-the-art FMIONet, while being 7850 times faster than numerical simulators.
Underground hydrogen storage (UHS) is a promising energy storage option for the current energy transition to a low-carbon economy. Fast modeling of hydrogen plume migration and pressure field evolution is crucial for UHS field management. In this study, we propose a new neural operator architecture, FFINO, as a fast surrogate model for multiphase flow problems in UHS. We parameterize experimental relative permeability curves reported in the literature and include them as key uncertainty parameters in the FFINO model. We also compare the FFINO model with the state-of-the-art FMIONet model through a comprehensive combination of metrics. Our new FFINO model has 38.1% fewer trainable parameters, 17.6% less training time, and 12% less GPU memory cost compared to FMIONet. The FFINO model also achieves a 9.8% accuracy improvement in predicting hydrogen plume in focused areas, and 18% higher RMSE in predicting pressure buildup. The inference time of the trained FFINO model is 7850 times faster than a numerical simulator, which makes it a competent substitute for numerical simulations of UHS problems with superior time efficiency.