Towards Fast GNN Surrogates for CO2 Migration in Complex Geological Formations

2026-06-15

AI Summary

This study addresses the challenge of predicting CO₂ plume migration in complex geological formations by proposing an end-to-end graph neural network surrogate model. The approach represents geological grids as graph structures enriched with geometric attributes and introduces a novel geometrically conditioned edge embedding to drive anisotropic message passing, effectively capturing directional transport behaviors induced by grid topology, permeability contrasts, and geological heterogeneity. By integrating autoregressive residual modeling with multi-step supervised training, the model achieves high-fidelity spatiotemporal simulation of multiphase flow in porous media. Evaluated on the SPE11A benchmark, the method demonstrates significantly lower cumulative errors in long-term predictions of gas-phase saturation and liquid-phase density compared to existing approaches, highlighting its superior generalization capability and numerical stability.

Abstract

This chapter discusses how a data-driven machine learning approach can reproduce key aspects of the physical behavior of multiphase flows in complex geological formations. We propose an end-to-end graph neural surrogate tailored to CO$_2$ plume migration forecasting in geological storage. The method is evaluated on the SPE11A benchmark, a well-known industry test case designed to assess CO$_2$ storage scenarios and characterized by sharp gas-water interfaces, strong advective transport, and rapid convective mixing with fingering development. The benchmark is reformulated as a graph in which nodes represent computational cells and edges encode transmissibility-based interactions enriched with geometric attributes. Directional transport arising from grid geometry, permeability contrasts, and geological heterogeneity is captured through an anisotropic message-passing mechanism, where interaction weights are computed via geometry-conditioned edge embeddings, biasing message aggregation toward physically relevant transport directions. Temporal evolution is modeled in latent space using an autoregressive residual formulation trained with multi-step supervision. The proposed model produces competitive forecasts of gas saturation and liquid-phase density, which are key indicators for CO$_2$ storage monitoring, with cumulative errors that remain moderate over extended forecasting horizons.

Problem

• CO2 migration
• geological storage
• multiphase flow
• surrogate modeling
• graph neural networks

Innovation

• graph neural network
• anisotropic message passing
• CO2 plume migration
• geological storage
• surrogate modeling