A GNN surrogate with geometry-conditioned anisotropic message passing and autoregressive residual training produces competitive forecasts of gas saturation and liquid density for CO2 storage on the SPE11A benchmark with moderate cumulative errors over long horizons.
Journal of Open Source Software10(105), 7357 (2025)
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Towards Fast GNN Surrogates for CO2 Migration in Complex Geological Formations
A GNN surrogate with geometry-conditioned anisotropic message passing and autoregressive residual training produces competitive forecasts of gas saturation and liquid density for CO2 storage on the SPE11A benchmark with moderate cumulative errors over long horizons.