Abstract

Hydrogen, as a clean and efficient energy source, is important in achieving zero-CO2 targets. This paper explores the potential of hydrogen geologic storage (HGS) in China for large-scale energy storage, crucial for stabilizing intermittent renewable energy sources and managing peak demand. Despite its promise, HGS faces challenges due to hydrogen's low density and viscosity, and its complex interactions with geological formations and microorganisms. This review offers a comprehensive overview of the current research status for HGS, with a particular focus on highlighting the main challenges confronting China. These difficulties and challenges primarily arise from complex geological conditions and the absence of fundamental parameters within potential reservoirs, including depleted oil/gas fields, salt caverns, and brine aquifers. Additionally, we have synthesized the current applications of machine learning (ML)as a potential solution. Key challenges are examined, such as the effects of operational parameters (e.g., cyclical injection-production and injection rates) on HGS efficiency, which can influence phenomena such as hydrogen fingering and caprock integrity. This review also looks into the insufficiently understood hydrogen-water-rock geochemical reactions under diverse temperatures and pressures, a gap that hampers the development of predictive numerical simulations and raises concerns about hydrogen leakage due to changes in porosity and permeability. Additionally, the paper addresses the limited knowledge about the metabolic mechanisms of subsurface microorganisms under extreme conditions, highlighting potential risks of hydrogen leakage and groundwater contamination. These microorganisms can metabolize hydrogen, producing gases such as CH4 and H2S, which may cause steel corrosion. Furthermore, the study assesses the distribution and prospects of three primary methods for pure hydrogen storage in China, considering the current state of hydrogen development and relevant government policies. The role of ML in advancing HGS is also discussed, offering insights into future research directions. This review not only scrutinizes the scientific challenges of HGS but also underscores its potential, guiding future simulation and practical engineering applications in this field.

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