Qingzhuo Liao

Professor

China University of Petroleum at Beijing

Qinzhuo Liao is a Professor in the College of Petroleum Engineering, China University of Petroleum-Beijing. From 2017 to 2021, he was an Assistant Professor in the Department of Petroleum Engineering, King Fahd University of Petroleum and Minerals. From 2015 to 2017, he was a postdoctoral fellow in Energy Resources Engineering, Stanford University and Peking University. He holds a BS degree in Mechanical Engineering from Peking University and a PhD degree in Petroleum Engineering from University of Southern California. His research interests include machine learning, reservoir simulation, surrogate modeling and upscaling, underground hydrogen storage. He was given the China's Excellent Young Scientists Fund Program (Overseas), SPE Journal Outstanding Technical Editor, Green Mining Science and Technology Award, and Petroleum Engineering Rock Mechanics Top 10 Scientific and Technological Progress Award. As the PI, he has undertaken more than 10 scientific research projects, from the Saudi Basic Science Foundation and the National Natural Science Foundation of China. He has published more than 60 SCI papers as the first/corresponding author, and has given 7 keynote/invited speaks on international conferences.

Participates in

TECHNICAL PROGRAMME | Energy Fuels and Molecules

Fueling the Future: Innovations & Strategies for Tomorrow’s Electricity Supply
Forum 13 | Technical Programme Hall 3
27
April
13:30 15:00
UTC+3
Hydrogen has emerged as a cornerstone of future low-carbon energy systems, offering clean, high-density, and versatile energy storage across power, transportation, and industry sectors. As hydrogen deployment accelerates globally, the development of large-scale, long-duration storage solutions becomes increasingly critical to bridge temporal mismatches between intermittent renewable generation and stable energy demand. Underground hydrogen storage (UHS) in depleted gas reservoirs presents a technically viable and geologically abundant solution, with high storage capacity, proven seal structures, and existing infrastructure enabling rapid deployment. However, subsurface hydrogen storage introduces complex multiphysics interactions—particularly thermal, hydraulic, chemical, and mechanical (THMC) couplings—that challenge storage efficiency, reservoir stability, and long-term system integrity, especially under cyclic injection–production conditions.

This study establishes a fully coupled numerical modeling framework to simulate THMC responses during repeated hydrogen injection–production cycles in porous and fractured formations. The model integrates thermal conduction, multiphase flow governed by Darcy's law, hydrogen–mineral geochemical interactions based on kinetic and equilibrium reactions, porosity and permeability evolution linked to mineral dissolution and precipitation, and mechanical deformation driven by stress redistribution. Rock deformation is modeled using stress–strain relationships that account for pressure sensitivity and thermoelastic effects. The coupling strategy allows for dynamic feedback among heat transfer, fluid migration, chemical alteration, and geomechanical responses. Boundary conditions such as temperature gradients, injection pressure schedules, fluid composition, and initial mineralogical heterogeneity are systematically varied to assess their impact on reservoir evolution and hydrogen storage performance.

Simulation results show that hydrogen–rock reactions, particularly those involving carbonates and sulfates, can lead to mineral dissolution, secondary precipitation, and spatial heterogeneity in pore structure. These geochemical changes alter permeability fields and modulate pressure propagation. Concurrently, thermal effects such as local heating enhance reaction rates, while stress redistribution causes compaction or dilation, especially in weakly cemented zones. The interaction between these coupled fields results in asymmetric hydrogen plume evolution, reduced injectivity, and localized sealing risk.

The THMC model provides a quantitative tool to evaluate storage performance and containment safety under complex geologic and operational conditions. The findings contribute to a deeper understanding of reservoir evolution during hydrogen cycling and offer technical insights for optimizing UHS system design, operational strategy, and long-term risk mitigation.