Towards Fast GNN Surrogates for CO2 Migration in Complex Geological Formations

Researchers propose an end-to-end graph neural surrogate for forecasting CO2 plume migration in geological storage, evaluated on the SPE11A benchmark. The model uses an anisotropic message-passing mechanism to capture directional transport and autoregressive residual formulation for temporal evolution, producing competitive forecasts of gas saturation and liquid-phase density with moderate cumulative errors over extended horizons.

arXiv:2606.17180v1 Announce Type: new 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.