Evaluation and re-understanding of the global natural gas hydrate resources
Bibliographic record
Abstract
Abstract Natural gas hydrate (NGH) has been widely considered as an alternative to conventional oil and gas resources in the future energy resource supply since Trofimuk’s first resource assessment in 1973. At least 29 global estimates have been published from various studies so far, among which 24 estimates are greater than the total conventional gas resources. If drawn in chronological order, the 29 historical resource estimates show a clear downward trend, reflecting the changes in our perception with respect to its resource potential with increasing our knowledge on the NGH with time. A time series of the 29 estimates was used to establish a statistical model for predict the future trend. The model produces an expected resource value of 41.46 × 1012 m3 at the year of 2050. The statistical trend projected future gas hydrate resource is only about 10% of total natural gas resource in conventional reservoir, consistent with estimates of global technically recoverable resources (TRR) in gas hydrate from Monte Carlo technique based on volumetric and material balance approaches. Considering the technical challenges and high cost in commercial production and the lack of competitive advantages compared with rapid growing unconventional and renewable resources, only those on the very top of the gas hydrate resource pyramid will be added to future energy supply. It is unlikely that the NGH will be the major energy source in the future.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".