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Record W7164138955 · doi:10.52843/cassyni.dtxg59

Session 7B: Natural Hydrogen

2025· article· W7164138955 on OpenAlexaff
Simon Holford, Rūta Karolytė, Mutah Musa, Florian Osselin, Jon Gluyas

Bibliographic record

Venuenot available
Typearticle
Language
FieldEnvironmental Science
TopicCO2 Sequestration and Geologic Interactions
Canadian institutionsDiscovery Centre
Fundersnot available
KeywordsHydrogen productionNatural gasHydrogenOphioliteUltramafic rockSedimentary rockDrillingDrilling fluid

Abstract

fetched live from OpenAlex

This seminar session addressed the emerging field of natural hydrogen (“white” or “gold” hydrogen) generated by geological processes in the Earth’s subsurface, highlighting its potential as a low-emission energy source. The presentations encompassed exploration frameworks, techno-economic assessments, and engineered mineral hydrogen production. A geoscience-driven exploration approach was outlined, emphasizing the identification of hydrogen-generating source rocks—primarily via serpentinization in ultramafic rocks and radiolysis in radioactive granitic terrains—and the assessment of hydrogen migration, trapping, and preservation within sedimentary systems. Integration of multi-physics geophysical data (gravity, magnetic, electrical) with geological and geochemical observations enables delineation of prospective basement terrains under cover. Techno-economic modeling from an Australian perspective evaluated a natural hydrogen production scenario involving a 650 km² reservoir with 10 wells, estimating capital expenditures around AUD 207 million and a levelized cost of hydrogen near AUD 2.86/kg. Sensitivity analyses identified hydrogen concentration in the gas mixture and production flowrate as critical cost drivers. Complementing natural occurrences, engineered mineral hydrogen production was presented as an approach involving injection of water and catalysts into iron-bearing ultramafic or magnetite-rich formations to accelerate abiotic hydrogen generation via iron oxidation. This method avoids hydraulic fracturing, relies on controlled temperature and catalytic enhancement, and targets abundant geological formations such as ophiolites and banded iron formations globally. Challenges remain in reaction kinetics, multiphase transport, reservoir management, and subsurface monitoring. The session underscored the need for drilling campaigns and technological validation within the next few years to confirm commercial viability and support hydrogen’s role in decarbonization pathways. Welcome from the Chair Migration dynamics of natural hydrogen and co-genetic gases Techno-economic assessment of natural hydrogen production: an Australia perspective Hydrogen: An Orange Solution for the Green Transition Discussion

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.225
Threshold uncertainty score0.754

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0020.000
Scholarly communication0.0030.002
Open science0.0020.004
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.2250.093

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.

Opus teacher head0.008
GPT teacher head0.278
Teacher spread0.269 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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