Lubricants Enabled Completion of ERD Well
Bibliographic record
Abstract
Abstract Lubricants are often added to the completion or drilling fluid, in order to reduce friction between tubular equipment run into the well, and the wall of the well. This paper shows in detail how the addition of a lubricant to an oil based drilling fluid affected the lubricity significantly in the second of two comparable drill pipe runs, resulting in historically low coefficients of friction. The use of an ultra-low viscosity drilling fluid resulted in a clean metal to metal contact between the casing and the drill pipe, which caused high static friction. The selected lubricant was designed to stick to metal surfaces, and the result showed that the lubricant had a significant effect on both torque and drag forces. In the drilling phase, the apparent static friction factor decreased from 0.23 to 0.13 and the dynamic friction factor from 0.13 to 0.11. Comparing the two 8 ½" sections drilled, the average off-bottom torque was reduced by 25 % after adding this specific lubricant to the drilling fluid. In selecting the lubricant, different lubricants were tested using a novel laboratory instrument capable of measuring the coefficient of friction at elevated pressure and temperature. These laboratory measurements proved to be consistent with the observed effect of the lubricants in the field.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".