PFC-2D Numerical Study of the Influence of Passive Vibration Assisted Rotary Drilling Tool (pVARD) on Drilling Performance Enhancement
Bibliographic record
Abstract
The objective of this work is to evaluate the influence of the implementing the downhole Passive Vibration Assisting Rotary Drilling (pVARD) Tool on enhancing drilling performance using a numerical study utilizing a Particle Flow Code (PFC-2D). The work is comprised of a numerical study of a simulation using the PFC-2D on an experimental work described in ARMA 15-492 (Rana et al, 2015). The numerical study was performed to validate the experimental work following the steps, procedure, and conditions performed in the laboratory work. The numerical study of the laboratory work involves not only the evaluation of drilling rate of penetration (ROP), but it also includes the Depth of Cut (DOC) of the bit cutters and the Mechanical Specific Energy (MSE). This numerical work also includes comparison study of drilling performance under various configurations of the pVARD tool, which represents a controlled downhole vibration against the rigid drilling configuration that represents the conventional rotary drilling. The pVARD configurations involves pVARD low spring compliance, medium spring compliance, and high spring compliance. The drilling output parameters of DOC, MSE, and ROP are then studied and analyzed in all pVARD and non-pVARD configurations. Likewise of the experimental work, the result of the numerical simulation approves the experimental work and it indicates the positive effect of utilizing the downhole pVARD on improving ROP. The drilling performance enhancement is also supported by the DOC and the MSE result.
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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) | 0.000 | 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".