Real-Time Bit Wear Optimization Using the Intelligent Drilling Advisory System
Bibliographic record
Abstract
Abstract Real-time tools and techniques have recently been used in drilling operations to minimize time and cost, thus improving drilling performance. The real-time drilling data transmission and analysis, from a remote server to an office location, plays an important role in the drilling optimization process. The drilling engineers can provide their expert opinions to the rig personnel, thus increasing drilling efficiency as well as reducing the associated risks. It has been shown that associated drill bit problems can greatly affect total drilling efficiency. Among them, bit wear while the bit is still in the hole can highly influence the operation if it is not properly analyzed. This paper describes the real-time application of a developed model for bit wear analysis. The model was developed based on the differences between rock energy models, Mechanical Specific Energy (MSE), and rock drillability from rate of penetration models. It has been modified and implemented as an engineering module in the newly developed software, Intelligent Drilling Advisory system (IDA's), and is used to estimate real-time bit wear for both rollercone and PDC bits. The drilling data is retrieved by the software from a remote server for the analysis. The data is subsequently quality controlled before calculating instantaneous bit wear while the bit is in the hole. In this study, bit runs for two offset wells in Alberta, Canada, will be analyzed in detail using the software module. Similarities between the recorded bit wear outs reported in the field and the simulation results indicate that the procedure can be used for bit wear estimation with good accuracy. This engineering software module could be used to identify unnecessary tripping which will result in time and cost reductions as well as an additional tool to aid in estimating bit wear status while drilling.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".