Comparison of MeshGraphNet Techniques for Subsurface Behavior Prediction during CO2 Sequestration

Conference · Mon Aug 05 00:00:00 EDT 2024

DOI:https://doi.org/10.2172/2426311· OSTI ID:2426311

Holcomb, Paul ^[1]; Shih, Chung Yan ^[1]; Sun, Alexander ^[1]; Liu, Guoxiang ^[2]; Siriwardane, Hema ^[2]

---

1. NETL Site Support Contractor, National Energy Technology Laboratory
2. NETL

Carbon sequestration is a vital part of the effort to mitigate anthropogenic climate change. Previously, we have shown that Graph Neural Networks (GNNs) provide the ability to extract meaningful insights during prediction of subsurface behavior in carbon storage projects. However, these models have struggled with long-term prediction accuracy due to error accumulation caused by autoregressive prediction. This research leverages the Illinois Basin – Decatur Project (IBDP) dataset to examine strategies for minimizing loss over time in a MeshGraphNet GNN model to improve reliability of predictions while minimizing inferencing time.

• Research Organization: National Energy Technology Laboratory (NETL), Pittsburgh, PA, Morgantown, WV, and Albany, OR (United States)
• Sponsoring Organization: USDOE Office of Fossil Energy and Carbon Management (FECM), Office of Carbon Management (FE-20)
• OSTI ID: 2426311
• Country of Publication: United States
• Language: English