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arXiv cs.LG
arXiv cs.LG
6/17/2026
Towards Fast GNN Surrogates for CO2 Migration in Complex Geological Formations

Towards Fast GNN Surrogates for CO2 Migration in Complex Geological Formations

Short summary

Researchers develop a graph neural network surrogate that forecasts CO2 plume migration in geological formations with reduced computational cost. The model uses geometry-conditioned message passing to capture multiphase flow physics, achieving competitive predictions of gas saturation and density on the SPE11A benchmark—critical for CO2 storage monitoring.

  • •Graph neural networks efficiently model CO2 plume migration in geological storage
  • •Geometry-conditioned message passing captures complex multiphase flow physics
  • •Surrogate model produces accurate predictions for industrial CO2 storage monitoring

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