Advancing Graph Neural Networks for CO2 Plume Migration in Complex Geological Formations

Resumo

Carbon Capture and Storage (CCS) is essential for mitigating climate change by permanently storing industrial CO2 emissions in subsurface formations [1]. As net-zero targets gain urgency, accurately predicting injected CO2 behavior over long timeframes becomes critical for ensuring storage integrity and regulatory compliance. Simulating CO2 plume migration presents significant computational challenges. Conventional numerical simulators, while accurate, require prohibitive computational resources when modeling the centuries-long timescales relevant to CO2 storage, especially for uncertainty quantification or optimization studies requiring multiple simulations. Deep learning surrogate models offer orders-of-magnitude speedup while maintaining reasonable accuracy [2].Current literature features two main surrogate modeling approaches: Fourier Neural Operators (FNO) [2] and Graph Neural Networks (GNN) [3]. FNO models perform well on structured domains but are limited to Cartesian grids, inadequately representing complex geological features. In contrast, GNN approaches like MeshGraphNet (MGN) [4] can naturally handle unstructured meshes, better representing heterogeneous subsurface environments and geological boundaries critical to CO2 migration.We present our MGN-LSTM implementation for temporal prediction of CO2 plume migration in faulted reservoirs [3], incorporating algorithmic improvements for enhanced prediction stability and efficiency. Our key contribution is integrating physics-aware loss terms from FNO literature [5] into the GNN framework. By incorporating mass conservation, phase behavior, and Darcy flow principles directly into the loss function, we achieve physically consistent predictions even with limited training data. These physics-aware constraints improve model generalization to unseen geological configurations.References[1] IPCC. Special Report on Carbon Dioxide Capture and Storage. CUP, 2005.[2] Wen, G. et al. Towards a predictor for CO2 plume migration using deep neural networks. Int. J. Greenhouse Gas Control, 2021.[3] Ju, X et al Learning CO2 plume migration in faulted reservoirs with Graph Neural Networks. Computers & Geosciences, 193, 2024.[4] Pfaff, T. et al. Learning mesh-based simulation with graph networks. ICML, 2021.[5] Badawi, D., Gildin, E. Neural operator-based proxy for reservoir simulations considering varying well settings, locations, and permeability fields. J. Petroleum Science and Engineering, 2023.