Towards Fast GNN Surrogates for CO2 Migration in Complex Geological Formations
This paper proposes an end-to-end graph neural surrogate for predicting CO2 plume migration in geological storage. Evaluated on the SPE11A benchmark, the model uses anisotropic message passing and autoregressive residual formulation to achieve competitive forecasts with low cumulative error over long horizons.
[2606.17180] Towards Fast GNN Surrogates for CO2 Migration in Complex Geological Formations
[Submitted on 15 Jun 2026]
Title:Towards Fast GNN Surrogates for CO2 Migration in Complex Geological Formations
View a PDF of the paper titled Towards Fast GNN Surrogates for CO2 Migration in Complex Geological Formations, by Rodrigo S. Luna and 10 other authors
View PDF HTML (experimental)
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)
Submission history
From: Rodrigo De Sapienza Luna Luna [view email] [v1] Mon, 15 Jun 2026 18:19:17 UTC (5,380 KB)
Full-text links:
Access Paper:
View a PDF of the paper titled Towards Fast GNN Surrogates for CO2 Migration in Complex Geological Formations, by Rodrigo S. Luna and 10 other authors
View PDF
HTML (experimental)
TeX Source
view license
Current browse context:
cs.LG
new | recent | 2026-06
Change to browse by:
cs
References & Citations
NASA ADS
Google Scholar
Semantic Scholar
Loading...
Data provided by:
Bibliographic Tools
Bibliographic and Citation Tools
Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
Connected Papers Toggle
Connected Papers (What is Connected Papers?)
Litmaps Toggle
Litmaps (What is Litmaps?)
scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
Code, Data, Media
Code, Data and Media Associated with this Article
alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub Toggle
DagsHub (What is DagsHub?)
GotitPub Toggle
Gotit.pub (What is GotitPub?)
Huggingface Toggle
Hugging Face (What is Huggingface?)
ScienceCast Toggle
ScienceCast (What is ScienceCast?)
Demos
Demos
Replicate Toggle
Replicate (What is Replicate?)
Spaces Toggle
Hugging Face Spaces (What is Spaces?)
Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
Related Papers
Recommenders and Search Tools
Link to Influence Flower
Influence Flower (What are Influence Flowers?)
Core recommender toggle
CORE Recommender (What is CORE?)
IArxiv recommender toggle
IArxiv Recommender (What is IArxiv?)
Author
Venue
Institution
Topic
About arXivLabs
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)