Optimization of Rock Breaking Parameters by Combing Mechanical Specific Energy Theory and Random Forest Algorithm
Bibliographic record
Abstract
Abstract As the key indicators to evaluate drilling efficiency, mechanical rate of penetration (ROP) and mechanical specific energy (MSE) are affected by many uncertain factors. In the actual drilling process, it is often necessary to adjust the parameters to achieve speed and efficiency. A dual-objective model is established considering the maximization of ROP and the minimization of MSE. Which combined with the theory of mechanical specific energy and the principle of multi-objective optimization, the model parameter weights are determined by the feature importance in the random forest regression model, and the value range of rock breaking parameter is optimized, realize the fusion of physical model and data model. The results show that, in the four strata of well X, the average ROP of T2k1 stratum is increased by 48.6% and the average MSE is decreased by 26.6%, the average ROP of T1b3 stratum is increased by 89.9% and the average MSE is decreased by 33.8%, the average ROP of the T1b2 stratum was increased by 41.3% and the average MSE was decreased by 39.0%, the average ROP of the T1b1 stratum was increased by 29.2% and the average MSE was decreased by 37.3%, which met the dual objectives of maximizing the ROP and minimizing the MSE. Compared with the traditional single-objective optimization method, the established dual-objective model is more in line with the needs of complex drilling engineering, and the fusion of physical model and data model can reduce certain human error.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".