Bibliographic record
Abstract
Abstract Challenging wells have been drilled recently utilizing advanced real time tools and techniques to optimize drilling operation while reducing risk and increasing safety. Moreover, the real time tools and techniques help identify upcoming drilling problems using real time data before they occur. Real time drilling analysis begins when real time drilling data are available and transmitted to the office locations via a remote server. The data can then be interpreted and analyzed by the engineers implementing various models for appropriate decision making. Lately, real time bit wear estimation has been a challenge in drilling a well to reach to the highest drilling performance and avoid bringing serious problems to the bit. It has been shown that the combination of Mechanical Specific Energy (MSE) and drilling rate models can be used for real time bit wear estimation while drilling. As MSE does not take the bit wear effect into account while drilling rate or rate of penetration (ROP) models do, their difference can be used to monitor and identify bit wear status while bit is in the hole. This paper demonstrates a new form of a developed model to predict bit wear status while drilling which is built by combining rock energy (MSE) model with a newly developed drilling rate model for roller cone bits as well as a previously developed model for PDC bits. Rock confined compressive strength (CCS) is obtained from ROP models and used in conjunction with MSE values to predict bit wear trend. Several bit run sections from offset wells in Alberta, Canada were tested utilizing the model and final results are compared with the reported bit wear outs from the field. Encouraging results show that this methodology can be applied to detect changes in drilling efficiency by monitoring bit wear trend in real time while drilling.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".