About Hydrogen
We apply physics-guided machine learning to assess subsurface resources for hydrogen generation, storage, and delivery. Our workflows integrate geologic, geophysical, and operational data to inform safe, efficient, and scalable hydrogen projects.
- Exploration targeting with multi-modal data fusion
- Reservoir characterization and storage suitability
- Risk-aware production planning and optimization
- Monitoring, verification, and reporting analytics
Need subsurface intel?
Explore the Hydrogen & Helium Resource Hub
Dive into regulatory, technical, and academic references spanning geologic hydrogen exploration, underground storage, and helium leasing.
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Related publications
- DPFEHM: a differentiable subsurface physics simulator (2023) — D. O'Malley et al. — Journal of Open Source Software
- Numerical and Experimental Studies of Coupled THM Processes in Fractured Tight Rocks (2023) — A. Abdullah, H. U. Rashid, O. M. Olorode — 57th U.S. Rock Mechanics/Geomechanics Symposium
- Subsurface energy: Flow and reactive-transport in porous and fractured media (2020) — M.K. Mudunuru et al. — Handbook of Porous Materials (invited), World Scientific Publishers
- Randomization in Characterizing the Subsurface (2018) — Y. Lin et al. — SIAM News
- A high-performance workflow system for subsurface simulation (2014) — V.L. Freedman et al. — Environmental Modelling & Software