Date & Time: 24th October 2025. 09:00-10:00
Title: Underground hydrogen storage in porous media: Microbial controls and multiphase flow
Speaker: Raymond Mushabe has completed the research component of his PhD on underground hydrogen storage at the University of Bergen, in collaboration with Equinor laboratories at Sandsli. He holds an MSc in Petroleum Engineering from NTNU, specializing in reservoir engineering and petrophysics, and a BSc in Petroleum Geoscience and Production from Makerere University, Uganda. He plans to defend his dissertation later this year and is currently exploring opportunities in industry or academia.
Exploring clean energy storage beneath the surface.
Hydrogen is a clean energy source that can be stored underground to help balance energy supply from renewable sources such as wind and solar. In this webinar, Raymond Mushabe presented how microbes living in saline water within reservoir rocks may influence hydrogen storage and recovery. His research combines laboratory experiments, imaging techniques, and computational models to improve underground hydrogen storage efficiency and support the development of reliable clean energy systems.
Raymond’s work spans core-scale experiments to reservoir-scale simulations, integrating MRI and PET imaging with machine learning models to predict hydrogen storage performance. His research addresses key challenges in subsurface hydrogen storage, including:
• Microbial hydrogen consumption and its impact on storage efficiency.
• Design and use of custom anaerobic setups for cyclic injection and storage experiments.
• In situ imaging to visualize pore and core-scale dynamics and microbial activity.
• Machine learning applications for fast and accurate reservoir predictions and design of laboratory experiments.
Key findings include:
• Microbial hydrogen losses are significantly higher in porous media than in traditional batch tests. One to two orders of magnitude higher but lasting for shorter intervals.
• Consumption rates decline over repeated storage cycles, suggesting potential for stable and efficient long-term storage.
• MRI imaging reveals distinct flow patterns and saturation dynamics between sterile and non-sterile environments.
• Machine learning models can effectively complement reservoir simulations, reducing computational costs and aiding experimental design.
Raymond’s interdisciplinary approach combines reservoir engineering, petrophysics, biogeochemistry, and data science, offering valuable insights for the future of low-carbon energy storage.