Subsurface Storage Technological Advancements & Innovation for Hydrogen (SUSTAIN H2)
Bibliographic record
Abstract
ABSTRACT: The Sustain H2 program is a cross-functional initiative focused on accelerating the safe and sustainable adoption of Underground Hydrogen Storage (UHS) - a critical enabler for long-duration, low-carbon energy storage. While hydrogen is gaining momentum across transportation and industry, large-scale storage remains a challenge. Sustain H2 targets porous rock formations, which make up 80% of global subsurface storage potential, yet remain underutilized compared to salt caverns. By integrating advanced reservoir simulations, experimental testing, and techno-economic analysis, the program is building a robust framework to evaluate site feasibility, control hydrogen migration, and ensure operational safety and regulatory compliance. This talk The Sustain H2 program is a collaborative initiative aimed at promoting the safe and sustainable adoption of Underground Hydrogen Storage (UHS), which is essential for long-duration, low-carbon energy storage. Although hydrogen is becoming increasingly important in transportation and industry, large-scale storage poses a challenge. Sustain H2 focuses on utilizing porous rock formations, which account for 80% of the global potential for subsurface storage, yet are currently underutilized compared to salt caverns. By combining advanced reservoir simulations, experimental testing, and techno-economic analysis, the program is developing a comprehensive framework to assess site feasibility, manage hydrogen migration, and ensure both operational safety and regulatory compliance. This talk will introduce Sustain H2’s vision and recent advancements while highlighting opportunities for collaboration across sectors to unlock the next frontier in hydrogen storage.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".