Review and Outlook on Fracturing Technology and Mechanism of Hydrate-Bearing Sediments
Bibliographic record
Abstract
Natural gas hydrates (NGHs) are widely distributed in marine and permafrost regions with huge reserves, which are considered one of the important potential sources for future clean energy. At present, China, Japan, the United States, Canada, etc. have conducted several trials; however, they all face varying degrees of challenges, such as low gas production efficiency and discontinuous production periods. In the oil and gas industry, hydraulic fracturing is a mature and highly efficient method for enhancing production through pressurization. Therefore, the successful application of fracturing technology to the NGH reservior is an urgently needed solution and could be a potentially revolutionary technology. This study summarizes the main recent fracturing advances in the hydrate field; it outlines the existing fracturing equipment for the NGH reservoir that differs from traditional oil and gas reservoir development, discussing the more efficient numerical simulation methods from the unit cell, experimental scale, to field scale. Additionally, it investigates the main controlling factors of fracturing behavior, such as the effects of fracturing fluid (viscosity and injection rate) and sample conditions (saturation, stress anisotropy, matrix, and natural fractures). The relationships and mechanisms proposed herein can provide new insights for understanding the fracturing behavior during hydrate exploration and constructing safe fracturing and extraction technologies.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".