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arXiv · 2606.17180

Towards Fast GNN Surrogates for CO2 Migration in Complex Geological Formations

Rodrigo S. Luna·Thiago H. N. Coelho·Luiz S. L. Neto·Roberto M. Velho·Adriano M. A. Cortes·Renato N. Elias·Alexandre G. Evsukoff·Fernando A. Rochinha·Mauricio Araya-Polo·Herve Gross·Alvaro L. G. A. Coutinho
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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.

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Rodrigo S. Luna, Thiago H. N. Coelho, Luiz S. L. Neto, Roberto M. Velho, Adriano M. A. Cortes, Renato N. Elias, Alexandre G. Evsukoff, Fernando A. Rochinha, Mauricio Araya-Polo, Herve Gross, Alvaro L. G. A. Coutinho. 2026-06-15. Towards Fast GNN Surrogates for CO2 Migration in Complex Geological Formations. https://arxiv.org/abs/2606.17180

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