Application of Gray Analysis Method in Friction Coefficient Assessment for Extended Reach Well
Bibliographic record
Abstract
The friction & torque is a core issue in the process of extended reach well drilling. The friction coefficient has a great influence on friction & torque prediction. Reasonable and correct determination of the friction coefficient is an issue that must be addressed in the friction & torque analysis and prediction. The inverse model of friction coefficient was established based on extensive actual drilling data. On this basis, grey relational analysis is used to explore the potential factors influencing the friction coefficient and establish the association between friction coefficient and the relevant influencing factors. The studying result indicates that the main factors affecting friction coefficient in terms of importance are the average rate of overall angle change, the type of drilling fluid, well depth, hole diameter, drilling fluid density, drilling fluid loss, dynamic shear of drilling fluid, vertical depth, displacement, drilling fluid viscosity, drilling fluid plastic viscosity. The research results can provide a theoretical guide for reducing the friction and torque in the construction of extended reach wells. Key words: Extended reach well; Friction & torque; Grey relational analysis; Influencing factors; Drilling fluid
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".