Study on the Affection of Drilling Tools’ Abrasion to the Regular Pattern of Tensile Strength
Bibliographic record
Abstract
Drilling tool is a necessary tool in oil drilling engineering, its performance directly affects the penetration rate of drilling engineering, to a well, reasonable selection of drilling tools will be conductive to high-quality, efficient and fast drilling construction. Reasonable optimization of drilling tools, based on the field testing of drilling tools in use, and according to the results of field testing, correctly guide the selection of drilling tools. According to the different drilling conditions, the drilling tools should be reasonably allocated to match the drilling performance with the drilling conditions. Analyze the problems and causes of drilling tools found in testing, and determine the rational solutions and preventive measures. The abrasion and thinning of drilling tools often occurs in the drilling process of oil and gas, which affects the bearing capacity of drilling tools. Tensile load is one of the main load-bearing modes of drilling tools and an important evaluation index of drilling tool safety. By simplifying the drilling tool model, confirm the tensile strength of the wearing drilling tool and the stress state at different well depths, and reach the relationship between the wearing degree of drilling tool and the safe well depth.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".