A Novel Tribometer Designed to Evaluate Geological Sliding Contacts Lubricated by Drilling Muds
Bibliographic record
Abstract
Abstract Great interest in improving lubricity, or reducing friction, of drilling muds used for horizontal oil well drilling is motivated by increasing the horizontal reach that can be attained by a single drilling site. However, there are a limited number of commercially available devices that can be used to evaluate novel drilling mud solutions under sliding conditions that accurately replicate those encountered in the field, and those that are available are often prohibitively expensive. Here, the design of a low-cost lubricity meter, or tribometer, is documented. The purpose-built tribometer is capable of varying rotating speeds, applied normal loads, temperature, and counter surface materials. In particular, the counter surface of the tribometer can be either a steel surface, as is often used in the industrial lubricity meters available in corporate laboratories, or a geological core specimen taken from the drill site. The novel instrument was then used to evaluate four commercially available water-based drilling fluid lubricant additives, dissolved in distilled water, for both a steel-on-steel contact and a steel-on-rock contact. The steel-on-steel contact shows that the tribometer replicates the results of tests typically conducted in drilling fluid labs, thus verifying the performance of the newly developed tribometer. Additional results show that the friction and performance of the lubricant depend significantly on the materials used: steel-on-steel contacts show much lower friction than steel-on-sandstone contact. Finally, a weak dependence on the applied load is shown for a number of lubricant additives examined.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".