GOOD EXPERIENCES LEARNED FROM HOME AND ABROAD TO DEVELOP LARGE GAS FIELDS IN CHINA
Bibliographic record
Abstract
To develop large gas fields with the recoverable reserve of 1 tcm has already been on the agenda in Chinese gas industry, and so it can be expected soon huge gas fields with 10 tcm of gas recovery. After briefly introducing many experiences learned from the development of four huge gas fields in Urengoy, Yambury, Medvezhye (Russia), and Groningen (Holland), this paper pointed out 8 points of suggestion as follows: (1) a complete technical mode deploy as a whole, to perform in steps, and to develop in three-dimensional style should be adopted due to imbalance between large scale of gas fields exploitation and low speed of drilling and ground surface construction; (2) the optimization of well types, well completion, and well pattern and spacing should be significantly essential for the development of large gas fields; (3) both gas recovery rate and gas well production should be well controlled; (4) the determination of gas driving modes should be concerned as well; (5) dynamic monitoring should be always carried out through the whole process of exploitation; (6) it should be necessary to exploit gas reservoirs in good balance; (7) more positive techniques should be improved like putting the ax in the helve; (8) various countermeasures should be taken for the HSE executives while building up large gas fields.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".