{"slug": "towards-fast-gnn-surrogates-for-co2-migration-in-complex-geological-formations", "title": "Towards Fast GNN Surrogates for CO2 Migration in Complex Geological Formations", "summary": "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.", "body_md": "arXiv:2606.17180v1 Announce Type: new\nAbstract: 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.", "url": "https://wpnews.pro/news/towards-fast-gnn-surrogates-for-co2-migration-in-complex-geological-formations", "canonical_source": "https://arxiv.org/abs/2606.17180", "published_at": "2026-06-17 04:00:00+00:00", "updated_at": "2026-06-17 04:30:57.697176+00:00", "lang": "en", "topics": ["machine-learning", "neural-networks", "artificial-intelligence"], "entities": ["SPE11A"], "alternates": {"html": "https://wpnews.pro/news/towards-fast-gnn-surrogates-for-co2-migration-in-complex-geological-formations", "markdown": "https://wpnews.pro/news/towards-fast-gnn-surrogates-for-co2-migration-in-complex-geological-formations.md", "text": "https://wpnews.pro/news/towards-fast-gnn-surrogates-for-co2-migration-in-complex-geological-formations.txt", "jsonld": "https://wpnews.pro/news/towards-fast-gnn-surrogates-for-co2-migration-in-complex-geological-formations.jsonld"}}