Anti-collision Optimization Design Technology of Large Infill Cluster Well Group in Bohai Sea Artificial Island B
Bibliographic record
Abstract
In view of the difficulty and low efficiency in the design of collision prevention and obstacle avoidance in the large-scale infilling adjustment of the cluster well group in Bohai Sea artificial island B, based on the analysis of the overall situation and characteristics of the infilling well, accurate calibration of already drilled data, and using industry standard principles for cluster well platform optimization and anti-collision design, develop specific procedures for designing anti-collision tracks for large-scale infill wells, form anti-collision optimization design methods, optimize drilling sequence, slot matching relationship and anti-collision track design parameters. The track design and anti-collision analysis of 45 infill wells have been completed in the limited platform space through multiple slot adjustment and track optimization.The minimum value of the separation coefficient of each well meets the requirements of the industry standard minimum limit of 1.5, and 70% of them are concentrated above 1.7, which reduces the risk of anti-collision as a whole.The results show that the anti-collision optimization design technology of large-scale infill cluster well group can effectively solve the anti-collision design problem of large-scale infill adjustment of cluster well group, and help to improve the design quality and efficiency.
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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 | 0.001 | 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".