My research focuses on numerical modeling for subsurface energy systems, with underground hydrogen storage (UHS) as the central application. The work links thermodynamics, compositional multiphase flow, microbial reactive transport, and scientific machine learning across scales from phase-equilibrium calculations to reservoir-scale simulation.
A recurring theme is the trade-off between physical fidelity and computational efficiency: which numerical formulations remain accurate and robust under different coupling strengths, reaction time scales, and reservoir operating regimes?
From characterization to coupled simulation
Development of reservoir-scale models coupling two-phase multicomponent flow with microbial reactive transport for UHS.
Comparison of fully implicit and sequential/operator-splitting formulations in terms of accuracy, robustness, computational cost, and regime dependence.
Hybrid physics-based and deep-learning-assisted methods for phase stability analysis and initialization of equilibrium calculations.
Data-driven surrogate modeling and optimization for cyclic UHS operation in depleted gas reservoirs.
Field-scale interpretation experience that informs reservoir characterization, model construction, and uncertainty-aware decision making.
The current numerical-methods work compares fully implicit (FIM) and sequential non-iterative/operator-splitting (SNI) strategies for compositional flow coupled with microbial reactive transport. The comparison is organized around four questions:
Linear-solver and preconditioner improvements are treated as numerical infrastructure unless they materially alter this coupling-strategy comparison.