The effect of drill–pipe rotation on improving hole cleaning using polypropylene beads in water-based mud at different hole angles
Bibliographic record
Abstract
Abstract Hole cleaning is always a problem, particularly during drilling operations, and drilling fluid plays an important role in transporting drill cuttings through an annular section of wellbore to the surface. To transport the cuttings, a water-based mud with added polypropylene beads was selected since it is environmentally friendly and cost efficient. The polypropylene beads help to transport cuttings by providing an additional buoyancy force that lifts the cuttings to the surface via the influence of collision and drag forces. This experiment was performed using a 20 ft test section, 10 ppg drilling mud and 0.86 m/s annular velocity in a laboratory scale rig simulator, and the concentration of polypropylene beads was varied from 0 to 8 ppb. As the concentration of polypropylene increases, the cutting transport ratio also increases. It was observed that the fewest cuttings are lifted at a critical angle of 60°, followed by 45°, 30°, 90° and 0°. Additionally, cutting sizes had moderate effects on the cutting lifting efficiency, where smaller cutting sizes (0.5–1.0 mm) are easier to lift than larger cutting sizes (2.0–2.8 mm). Furthermore, a study of buoyancy force and impulsive force was conducted to investigate the cutting lifting efficiencies of various concentrations of polypropylene beads. This lifting capacity was also assisted by the presence of polyanionic cellulose (PAC), which increases the mud carrying capacity and is effective for smaller cuttings. The results show that in the presence of pipe rotation, the cutting lifting efficiency is slightly enhanced due to the orbital motion provided by the drill pipe for better hole cleaning. In conclusion, polypropylene beads combined with pipe rotation increase the cutting transport ratio in the wellbore.
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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".