An interpretable framework for predicting weight-on-bit in horizontal wells based on TCL-BO stacking ensemble
Bibliographic record
Abstract
The primary objective of this study was to develop an interpretable machine learning framework for accurate prediction of weight on bit (WOB) in horizontal wells. With the advancement of oil and gas technologies, horizontal drilling has become a key method for resource recovery, but drag in ultra-long sections reduces efficiency. Traditional WOB calculation methods rely on mathematical models and physical experiments, which are costly and complex. To address this, eight parameters, including well depth and hook load, were selected as model inputs. An ensemble tree model optimized by Bayesian algorithms was built to capture parameter – WOB relationships. SHapley Additive exPlanations (SHAP) were applied to interpret model outputs and assess parameter importance. Performance was evaluated using R2, MSE, MAE, and RMSE. The best-performing ensemble model was further integrated into a CNN-LSTM to enhance temporal feature learning. This hybrid approach achieved an R2 of 0.96, representing about a 10% improvement over standalone models. The proposed framework thus offers a reliable and interpretable tool for WOB prediction, providing valuable reference for improving drilling efficiency in horizontal well sections.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".