Study of the Influence of Controlled Axial Oscillations of pVARD on Generating Downhole Dynamic WOB and Improving Coring and Drilling Performance in Shale
Bibliographic record
Abstract
Abstract This work concentrates on the investigation of enhancing drilling performance through increasing drilling rate of penetration (ROP) by using a passive vibration assisted rotary drilling (pVARD) tool. It also involves analysis of how ROP was significantly increased when drilling using pVARD compared to drilling using conventional system “rigid” using coring and drilling in shale rocks. The apparatus used was a fully instrument laboratory scale rig and the bits were dual-cutter polycrystalline diamond compact (PDC) bit for drilling and diamond impregnated coring bit for coring. The flow rate was constant of (7 litter / min) using clean water at atmospheric pressure. In addition, for accuracy data recording, a data acquisition system (DAQ-Sys) using a LabVIEW software was utilized to record data at 1000HZ sampling rate. The output drilling parameters involved in the analysis included operational rpm, torque (TRQ), and ROP. All the output-drilling parameters were analyzed with relation to downhole dynamic weight on bit (DDWOB). The result of this work explained how pVARD can increase the DDWOB and improve ROP. The result also demonstrated generating a balanced and concentric increase in DDWOB and minimizing the wide-range fluctuation of DDWOB generated in rigid drilling, particularly at high DDWOB.
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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".