Torsional Vibration Analysis of Drillstrings in Blasthole Drilling
Bibliographic record
Abstract
Reducing vibrations in well drilling has a significant effect on improving the overall performance of the drilling process. Vibrations may affect the drilling process in different ways, i.e., reducing durability of the drillstring’s elements, reducing the rate of penetration, and deviating the drilling direction. In rotary drilling, which is used to open mine and oil wells, torsional vibration of the drillstring is an important component of the overall system’s vibration that has received less attention in the literature. In this paper, we propose a finite element model for a sample blasthole drillstring used to open mine wells to investigate its torsional vibrations. Boundary conditions and elements’ specifications are applied to this model. In the model, the interaction between the insert and the rock is represented by a set of repetitive impulses according to the insert pattern. The steady-state response of the system to the repetitive impulses is found and natural frequencies, kinetic energy, and potential energy of the drillstring are calculated. The root mean square (RMS) of the total energy can be used as the measure for reducing the torsional vibration of the system. Finally, an optimum combination of inserts on the cone’s rows was found based on minimizing the total vibratory energy of the drillstring. The optimum design can reduce the torsional vibrations of the drillstring and improve the drilling performance.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".