Real-Time Drill Bit Wear Prediction by Combining Rock Energy and Drilling Strength Concepts
Bibliographic record
Abstract
Abstract A central element to reduce drilling cost is to improve drilling operation by analyzing real-time data. Developing advanced real-time analysis tools is one way to improve the drilling operation. Two approaches which currently are used for optimizing the actual rotary drilling process are mechanical specific energy and inverted rate of penetration models. The mechanical specific energy method is defined as the work needed to destroy a given volume of the rock. It can act as a tool during the drilling operation to detect changes in drilling efficiency thus providing a method to optimize the drilling parameters to enhance instatanious rate of penetration. Rate of penetration models, on the other hand, can be used to calculate formation drillability considering the effects of drilling parameters, bits design and bit wear. Drilling optimization using rate of penetration models is done by changing the drilling parameters and/or bit design to find the optimum drilling scenario for an entire bit run. The mechanical specific energy log and the drillability ratio differ when mud weight is changed and when bits are worn. These two differences are due the fact that mechanical specific energy does not include bit wear as well as the effect of changing mud weight. By combining these methods and modifying the mechanical specific energy equations to incorporate these effects and the mechanical specific energy can be used as a real-time trending tool for bit wear estimations. In this analysis, wells from offshore Middle East and onshore North America are analyzed. The field results are very encouraging in that the bit wear for both roller cone and PDC bits can be predicted. The field validation of this new approach shows that the supplementary information on the bit wear status can in some cases benefit in the decision of when to pull the bit while it still is in the hole and thereby possibly improve overall economics of the drilling operation.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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".