Towards Fast GNN Surrogates for CO2 Migration in Complex Geological Formations
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Computer Science > Machine Learning
Title:Towards Fast GNN Surrogates for CO2 Migration in Complex Geological Formations
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.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2606.17180 [cs.LG] |
| (or arXiv:2606.17180v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.17180
arXiv-issued DOI via DataCite (pending registration)
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Submission history
From: Rodrigo De Sapienza Luna Luna [view email][v1] Mon, 15 Jun 2026 18:19:17 UTC (5,380 KB)
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