Bibliographic record
Abstract
Abstract Bearing failure of roller cone bits may result in a time-consuming fishing job, and lead to significant increase in drilling costs. The bearing failure generally comes from over wear of frictional pairs (surfaces between the journal and bearing of the cone). A bearing wear model has been developed to predict the wear status through multi-variable nonlinear regression analysis based on field data. The wear model considers four variables including weight on bit (WOB), revolution per minute (RPM), diameter of bit and hours drilled as a function of IADC bit bearing wear. Some abnormal bit run field reported bearing failures were removed in order to acquire the best regression of the field data. A bearing failure probability model is then introduced to predict the survival probability of the bit, the parameter of which is obtained through statistics of more than 500 bit runs. The wear status, including instantaneous and cumulative wear, for different roller cone bits and different wells drilled in Western Canada is simulated respectively with the wear model. A good correlation coefficient was obtained for different IADC bit types including both milled tooth and insert roller cone bits. The cumulative wear values from the model match close those from the field. The wear model and the failure probability model can help drilling engineers evaluate bearing wear status during real time drilling operations through simulation, and make a decision on when to pull out the bit in time to avoid bearing failures and the possibly lost cones. Better bearing wear predictability will result in better drilling results and effect the total drilling cost.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".