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Record W4409774419 · doi:10.29017/scog.v48i1.1691

The Emergence of Natural Hydrogen: Genesis and Current Perspectives

2025· article· en· W4409774419 on OpenAlexaboutno aff
D. S. Ranjit Singh

Bibliographic record

VenueScientific Contributions Oil and Gas · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstro and Planetary Science
Canadian institutionsnot available
Fundersnot available
KeywordsNatural (archaeology)Current (fluid)GeologyPaleontologyOceanography

Abstract

fetched live from OpenAlex

In recent years, natural hydrogen has emerged as a promising source in the future energy mix. Since its accidental discovery in Mali in 2012, global interest in this form of hydrogen—often called geological, white, or gold hydrogen—has surged. Generated primarily through serpentinization, a process where water reacts with iron-rich minerals, natural hydrogen can become trapped in reservoirs beneath impermeable rocks. Other natural processes also contribute to its formation. A recent study by French geologists and Albanian scientists found hydrogen with 85% purity in a chromium mine, with minimal methane contamination, resulting in a low carbon intensity of around 0.4 kg CO2e per kg of hydrogen produced. According to Rystad Energy, white hydrogen could potentially transform the clean hydrogen sector, shifting it from an energy carrier to a primary energy source. Interest in natural hydrogen is growing rapidly, with the number of companies involved, increasing from 10 in 2020 to 40 by the end of last year. Exploration is underway in countries like Australia, the U.S., Spain, France, Albania, and Canada. Canada-based Hydroma, for instance, extracts white hydrogen at just $0.50 per kg, while projects in Spain and Australia aim for around $1 per kg. This suggests that natural hydrogen could offer a cost-effective, low-carbon energy alternative. This article examines the potential of natural hydrogen to play a key role in achieving a net-zero carbon future by exploring its science, economics, and ongoing global exploration efforts.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.802
Threshold uncertainty score0.652

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.004
GPT teacher head0.238
Teacher spread0.234 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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

Citations1
Published2025
Admission routes1
Has abstractyes

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