Five-Section Trajectory Design of Thick Glutenite Reservoir in Shengli Oilfield
Bibliographic record
Abstract
Many blocks of Shengli Oilfield are located in urban areas, and the site selection of well sites is limited. In order to meet the needs of reservoir development and deployment, five-section trajectory is increasingly used. Difficulty in site selection results in directional well development, and reservoir deployment requires vertical well development. In order to resolve the two contradictions, five-section trajectory is used in the well design, and vertical drilling after hitting the target. The problems with this type of trajectory are high torque drag and easier fatigue of the drilling pipe. When the displacement is small, the effect is small. When the displacement is large, it will cause engineering complexity such as difficulty drilling weight transfer and fatigue of drilling pipe. Aiming at the shortcomings of the five-section trajectory, with the help of existing drill string force analysis software, the parameters of the five-section trajectory were analyzed, and reasonable values were recommended to provide an optimization idea for the five-section trajectory.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".