Underground Hydrogen Storage
“Unlocking the potential of the subsurface to store clean hydrogen safely and at scale.”
We develop frameworks for safe and efficient geological hydrogen storage in saline aquifers, depleted reservoirs, and ultramafic rocks. Our studies investigate geochemical alterations, thermo-hydro-mechanical responses, and microbial interactions that govern hydrogen storage performance. We also create machine learning and ranking tools to assess storage sites and predict storage capacity, efficiency, and well integrity.
Carbon Capture, Utilization, and Storage (CCUS)
“Transforming underground reservoirs into long-term carbon vaults for a low-carbon future.”
Our team advances CCUS by studying stress changes, wellbore cement integrity, and near-wellbore geomechanics during CO₂ injection. We also explore the synergies of reusing hydrocarbon wells for CO₂ storage and geothermal energy recovery, providing innovative pathways for decarbonizing existing infrastructure. In addition, our research develops analytical and numerical models to predict CO₂ plume migration under real geological conditions, helping to ensure long-term storage security.
Geothermal Energy Systems
“Harnessing the Earth’s natural heat to power tomorrow with clean, reliable energy.”
We apply coupled thermo-hydro-chemo-mechanical (THCM) models and techno-economic frameworks to evaluate geothermal systems, including enhanced geothermal systems (EGS) and CO₂ plume geothermal concepts. Our research assesses fracture aperture effects, recovery efficiency, and flexible geothermal operations (such as Huff-n-Puff cycles), driving innovations for clean baseload power generation.
Data-Driven Reservoir Engineering
“Turning data into smarter predictions for sustainable energy production.”
We harness machine learning, reduced-order modeling, and numerical simulation to improve predictions of subsurface processes. Applications include forecasting hydrogen column heights, geothermal drilling rates of penetration, CO₂ plume migration, and hydrocarbon recovery. Our co-optimization frameworks link machine learning with carbon sequestration and energy production strategies.
