C01-05

10:30 〜 10:45英語

[C02] Multiphysics Deep Learning for Spatiotemporal Carbon Storage Prediction Under Geological Uncertainty

○Hung Vo¹ (1. Waseda University)

キーワード：

CCS、Deep learning、Geological uncertainty

This work presents a deep learning–accelerated framework for emulating strongly coupled multiphysics processes in subsurface storage systems, enabling rapid field-scale screening and digital-twin workflows. High-fidelity numerical models that resolve multiphase flow, geomechanical deformation, and reactive transport remain the benchmark for assessing storage performance and integrity, yet their computational cost constrains uncertainty quantification, real-time optimization, and continuous monitoring. This study addresses this limitation by developing a multi-output surrogate that approximates the response of a fully coupled multiphysics simulator for fluid injection in heterogeneous, layered geological formations, delivering fast predictions of key state variables relevant to plume evolution, pressure–stress response, and deformation risk.