Dynamically Modelling Rock Failure in Percussion Drilling
Bibliographic record
Abstract
Percussion drilling attracts the oil and gas industries for its potential to provide faster rate of penetration (ROP) than traditional rotary drilling, especially in hard formations. However, frequent mechanical failures, poor understanding and therefore control of drilling operations have limited its applications in the field. The objective of this research is to advance the fundamental understandings of the physical mechanisms involved in percussion drilling, thereby facilitating more efficient and lower cost drilling and exploration of hard-rock reservoirs. A geomechanical model is presented in this paper to simulate the percussion drilling process. The numerical simulation for rock failure is based on a Mohr-Coulomb model with strain-softening behavior, Rayleigh damping to dissipate excessive oscillation energy, and a fatigue/damage algorithm to update rock properties due to cyclic loading. Important mechanisms for rock failure during percussion drilling, such as aggressive tensile failure due to wave reflection at the rock impact surface, compressive failure due to high axial loading stress, and rock fatigue due to cyclic loading, are captured in the simulation. The insights gained from this study improve the understanding of percussion drilling, and may facilitate development of a simulation tool to better characterize this promising technology.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".