CURE@Inha

Monitoring CO₂ plume migration, assessing wellbore integrity, and quantifying leakage risk in geological storage formations. We develop ML-assisted diagnostics and high-fidelity simulation frameworks for safe, long-term storage.

Investigating cyclic injection and withdrawal dynamics, geochemical reactions, and microbial effects in porous reservoirs. We model hydrogen behavior to maximize storage safety and energy recovery efficiency.

Reconstructing 3D pore microstructures from micro-CT imaging, simulating multiphase flow with lattice Boltzmann methods, and upscaling pore-scale properties to the reservoir scale for accurate formation evaluation.

Applying diffusion models, GANs, and SinFusion-based techniques to generate realistic geological realizations, augment scarce subsurface data, and enable physics-informed deep learning for seismic interpretation.

Deploying ensemble methods, surrogate models, and Bayesian frameworks to propagate geological uncertainty through reservoir simulations — enabling robust, risk-informed decisions for energy storage and production.