UTILIZING BASELINE GEOCHEMISTRY AND MICROBIOLOGY FROM FOUR DIFFERENT HYDROGEN STORAGE TARGET RESERVOIRS FOR PREDICATING THE IMPACTS OF SUBSURFACE HYDROGEN STORAGE

Hydrogen has been identified as a flexible energy carrier with zero or negative carbon emission across multiple energy sectors. For this reason, there is increased interest in developing or transitioning infrastructure for hydrogen transport and storage, including geologic storage of hydrogen gas. However, the safety, security and reliability of underground hydrogen gas storage have not been fully demonstrated. An important knowledge gap in this assessment includes the responses of subsurface microbial communities to stored hydrogen gas. With this, the extent and duration of deleterious microbial activities remain unknown and likely will be highly dependent on specific storage reservoir conditions. To understand the impact of microbial activity on underground hydrogen storage and the variability between different storage systems, the geochemistry and native microbial community must be characterized using relevant field samples.

Here, we assess the geochemistry and microbiology from 4 different candidate storage systems for underground hydrogen storage from two geospatially distinct regions. We completed qPCR, 16S rRNA sequencing, and geochemical analyses. Previous hydrogen storage work has linked subsurface microorganisms with methanogenesis, hydrogen sulfide production, acid production, and microbial corrosion. Our results demonstrate each system had a unique microbial community and geochemical signature, but microorganisms capable of hydrogen consumption were present in all four systems. Hydrogen consumption through sulfate reduction and iron reduction appear to be potential microbial processes in all four systems, and one system had a high potential for hydrogen consumption through acetate production. This work contributes to a growing baseline understanding of the biogeochemistry in future hydrogen storage sites that will be essential for predicting storage capacity and efficiency via experimental and modeling approaches or large-scale deployment.