A Data-Driven Deep Learning Framework for Microbial Reaction Prediction for Hydrogen Underground Storage

Abstract

As the use of hydrogen gas (H2) as a renewable energy carrier experiences a major boost, one of the key challenges for a constant supply is safe and cost-efficient storage of surplus H2 to bridge periods with low energy demand. For this purpose, underground gas storage (UGS) in salt caverns, deep aquifers and depleted oil-/gas reservoirs has been proposed, which provide suitable environments with potentially high microbial abundance and activity. Subsurface microorganisms can use H2 in their metabolism and thus may lead to a variety of undesired side effects such as H2 loss into formation, H2S build up, methane formation, acid formation, clogging and corrosion. We present a new AI framework for the determination of metabolism processes of subsurface microorganisms in hydrogen underground storage. The AI framework enables to determine the potential microbial related processes and reactions in order to optimize storage strategies as well as incorporate potential remediating actions to ensure efficient and safe underground hydrogen storage and minimize related side effects. We evaluated the framework on investigating potential microbial reactions for hydrogen storage in the Pohokura gas field in New Zealand. The framework evaluates reservoir parameters, such as temperature, pressure, salinity and hydrogen injection volumes as well as duration, and then classifies which reactions may take place as well as indicates the likelihood of the reaction taking place. For the deep learning framework, an optimized random forest algorithm was implemented and compared to other multi-class classification problems. The results demonstrated the ability to determine the microbial reactions that may take place with hydrogen storage reservoir as well as its severity, which enhances the optimization of injection strategy as well as suitability of a reservoir. This framework represents an innovative approach to microbial reaction prediction for underground hydrogen storage. The framework allows potential reservoirs to be efficiently evaluated and optimization strategies to be utilized in order to maximize the efficiency of underground hydrogen storage.

Integrating Capacity and Efficiency for Optimal Hydrogen Storage Site Selection in Saline Aquifers

“…The optimal H 2 storage schedule was presented by optimizing the H 2 recovery and NPV. Katterbauer et al 34 presented a framework to determine the metabolism process of subsurface microorganisms in undergound H 2 storage. The random forest algorithm was applied to conduct multiclass classification.…”

“…The XGBoost and random forest algorithms showed high prediction performance with R 2 > 0.95. However, the studies of Katterbauer et al and Thanh et al did not focus on H 2 storage capacity and efficiency. In contrast, extensive analyses − have investigated the uncertainty quantification and performance optimization of CO 2 sequestration using ROMs.…”