The Law of Liquid–Solid Carrying in the Wellbore of Natural Gas Hydrate Gas Well under the Condition of Foam Drainage Gas Recovery
Bibliographic record
Abstract
During the trial production of marine gas hydrate in the former Soviet Union, Canada, North Slope of Alaska, South Sea Trough of Japan and Shenhu Sea of South China Sea, the problem of sand and water production cannot be avoided. The problems of sand production and water production in the process of natural gas hydrate depressurization exploitation have seriously restricted the exploitation efficiency and production of natural gas hydrate. The problems of sand production and water production are some of the main factors that prevent natural gas hydrate being commercially exploited. Therefore, it is urgent to carry out useful, relevant and cutting-edge research on the efficient drainage of sand and water from the wellbore in the process of natural gas hydrate mining. This paper innovatively proposes liquid-carrying and solid-carrying technology under foam circulation purging to address the existing problems of sand removal and drainage technology in hydrate mining. At present, no scholar has used this technology to solve the problem of sand removal and drainage in hydrate mining. Therefore, the research on efficient drainage is imperative. In this paper, We mainly studied the liquid-carrying and solid-carrying of vertical wellbore under the condition of foam cycle purging. We have revealed the relevant the liquid-carrying law and solid-carrying law through the above research.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".