Deep Learning-Assisted Flash Calculations for Underground Hydrogen Storage
A hybrid thermodynamic and deep-learning workflow for accelerating flash calculations in underground hydrogen storage simulation.
Zhilei Han
I am a PhD candidate in Energy Resources and Petroleum Engineering at King Abdullah University of Science and Technology (KAUST). My doctoral research focuses on numerical modeling of coupled compositional multiphase flow and microbial reactive transport for underground hydrogen storage (UHS), with emphasis on thermodynamics, reservoir simulation, and computational efficiency.
Before my PhD, I worked for seven years at CNOOC Tianjin as a formation evaluation and logging engineer. This combination of field-scale formation evaluation experience and reservoir simulation research shapes my interest in integrated subsurface characterization, modeling, and decision support.
A hybrid thermodynamic and deep-learning workflow for accelerating flash calculations in underground hydrogen storage simulation.
Surrogate-model-based robust optimization for underground hydrogen storage in depleted natural gas reservoirs.
A coupled compositional-flow and microbial reactive-transport framework for underground hydrogen storage.