Experimental Investigation on the Production Behaviors of Methane Hydrate in Sandy Sediments by Different Depressurization Strategies
Bibliographic record
Abstract
Abstract The depressurization method is one of the most promising methods for the exploitation of hydrate reservoirs and has been conducted in several field tests. In this work, the production behaviors of methane hydrate in sand sediments by different depressurization strategies were comparatively investigated using a cubic hydrate simulator (CHS) with a capacity of 5.832 L. The experimental conditions are based on the hydrate reservoirs parameters of the South China Sea. The results indicate that the hydrate dissociation rate is related to production pressure and heat conduction between the sediments and the surroundings. Some hydrate form again at the beginning of depressurization and the progress of hydrate dissociation during stable depressurization stage is basically along with the P−T curve. The cumulative amounts of gas production are almost the same, affirming it depends on the final depressurization amplitude, and less water produced when a short shut‐in period is conducted before hydrate dissociation. Compared to single‐step depressurization strategy, the multistep depressurization possesses a more well‐distributed gas production, and the sensible heat of sediments contributes less to hydrate dissociation. Despite the production period is longer, its temperature over the whole production process is higher, which has the potential to reduce the secondary hydrate formation and the damage of wellbore and sediments effectively in actual hydrate production.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".