PDP-PINN: A Fourier-Augmented Physics-Informed Neural Network for Pressure Drop Prediction in CO₂-Injected Porous Media

Accurate prediction of pressure variations during CO2 injection is crucial for the safe and efficient operation of carbon capture and storage (CCS) in subsurface geological formations. Excessive pressure can compromise caprock integrity or create leakage pathways, posing significant operational risks. Traditional numerical simulations, while precise, are computationally intensive and often unsuitable for real-time applications. This study presents PDP-PINN, a Fourier-augmented physics-informed neural network designed for pressure drop prediction across heterogeneous porous media during CO2 injection. The model integrates spatio-temporal flow features with physical constraints derived from Darcy's law and employs Fourier feature embeddings to mitigate spectral bias. Evaluated on a high-resolution two-phase CO2-water simulation dataset, PDP-PINN achieves strong predictive performance across various heterogeneity levels, with a coefficient of determination ( R-2 ) up to 0.9941 and mean squared error (MSE) of 5.56, while maintaining physical consistency. Furthermore, explainable AI techniques, including SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME), are applied to provide global and local interpretability, revealing the influence of individual features on model predictions. The proposed architecture demonstrates potential for real-time injectivity assessment, flow resistance estimation, and subsurface risk mitigation in CCS operations.