Numerical simulation of underground CO2 storage requires accurate, fast, field-scale calculations of saturation and reservoir pressures, yet high-fidelity physics-based simulators are computationally expensive for rapid analysis. Recent advances in deep learning, especially those such as graph neural networks (GNNs), offer promising alternatives by approximating simulation outputs while reducing computational costs dramatically by minimizing the total reliance on traditional simulators. However, training GNN models on large-scale, spatiotemporal data sets remains a significant bottleneck when constrained to a single graphics processing unit (GPU). To address the computational bottleneck of training GNN on large‑scale and high-resolution data sets, the authors propose multi-GPU scalable training of GNNs.
Advantages of GNNs
GNNs are particularly well-suited for subsurface flow because they preserve the relational structure that governs connectivity and transmissibility. Through message passing, GNNs aggregate local information to form multiscale representations that capture both sharp saturation fronts and broad pressure support.
