Universal physics-based rate of penetration prediction model for rotary drilling
Bibliographic record
Abstract
The drilling process is one of the most important and expensive aspects of the oil and gas industry. Drilling is required during mining for different ore production processes such as blasting and large drilling operations. Overall, it contributes significantly to the total cost of mining. As a result, an accurate prediction of the rate of penetration (ROP) is crucial for drilling performance optimization and contributes directly to reducing drilling costs. Knowledge of drilling performance is a powerful tool to aid in the development of a consistent drilling plan as well as to anticipate issues that may arise during drilling operations. Several approaches, with varying degrees of complexity and accuracy, have been tested to predict drilling performance, but all have shown several limitation to predict the complete drilling performance curve including locate the founder point. This limitation can be extended to their capacity of covering different drilling scenarios with high accuracy. In this thesis (manuscript style) a review of the history of drilling performance prediction is conducted with emphasis on the rotary drilling of small and large diameters. The approaches are grouped into two categories: physics-based models and data-driven models. Due to the low complexity of the physics-based models and the scarcity of drilling performance prediction research that reports the founder point location, a novel physics-based ROP prediction model for rotary drilling that includes the founder point location is presented. This model presents high accuracy to predict the drilling performance for fixed cutter drill bit, roller-cone drill bit, and large diameter drilling operations. The behaviors of the new model constants (drillability coefficient and drillability constant term) are discussed when analyzed in relation to the unconfined compressive strength (UCS), bit diameter, and rotary speed. Additionally, a new experimental setup approach was developed based on the circular movement of the full-scale disc cutter that are normally used in raise boring and tunnel boring machines. This setup will permit to simulate the large diameter drilling operations in laboratory scale aiming the understanding of the fragmentation process and application of optimization to this scenario.
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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 | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".