Physics-Based Rate of the Penetration Prediction Model for Fixed Cutter Drill Bits
Bibliographic record
Abstract
Abstract The drilling process is one of the most important and expensive aspects of the oil and gas industry. Its economic feasibility is a direct relation to good planning that has high dependence on an accurate prediction of the rate of penetration (ROP). Knowledge of drilling performance through ROP prediction models is a vital tool in the development of a consistent drilling plan and allows industry players to anticipate issues that may occur during a drilling operation. Additionally, as some drilling parameters (such as rotary speed, weight on bit (WOB), and drilling fluid flowrate), an accurate prediction of the ROP is crucial to the optimization of drilling performance and contributes to reducing drilling costs. Several approaches to predict the drilling performance have been tried with varying degrees of success, complexity, and accuracy. In this paper, a review of the history of drilling performance prediction is conducted with emphasis on rotary drilling with fixed cutter drill bits. The approaches are grouped into two categories: physics-based and data-driven models. The paper’s main objective is to present an accurate model to predict the drilling performance of fixed cutter drill bits including the founder point location. This model was based on a physics-based approach due to its low complexity and good accuracy. This development is based on a quantitative analysis of drilling performance data produced by laboratory experiments. Additionally, the validation and applicability tests for the proposed model are discussed based on drill-off tests (DOTs) and field trials in several different drilling scenarios. The proposed model presented high accuracy to predict the fixed cutter drill bit drilling performance in the 27 different drilling scenarios which were analyzed in this paper.
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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 | 0.001 |
| 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".