Investigation on horizontal and deviated wellbore cleanout by hole cleaning device using <scp>CFD</scp> approach
Bibliographic record
Abstract
Abstract The application of the hole cleaning device in downhole is a new technology for removing cuttings bed, and it can increase the efficiency of cuttings transport. This paper mainly studies the effects of the helical angle and the rotational speed of the blade of the hole cleaning device on the swirl strength of decaying swirl flow and drilled cuttings deposition behavior. A three‐dimensional computational fluid dynamics (CFD) model is established using the Eulerian‐Eulerian two‐fluid model, Realizable k‐ε turbulence model, and Sliding Mesh technique for simulating the two‐phase fluid flow. The results have been compared with available data in the literature, and a good agreement is found. In order to understand the decay behavior of the swirl flow along the flow direction, the initial swirl strength, the swirl number, and the decay rate of swirl are analyzed in detail in single phase. The effects of the swirl flow induced by the blade on the deposition behavior of drilled cuttings under different rotational speed and helical angle are studied. A new deposition index is used to evaluate the effective distance and hole cleaning efficiency of the swirl flow under various parameters. It has been observed that using the hole cleaning device can improve the hole cleaning performance. The research results of this paper is instructive to the design of the hole cleaning device using in the drilling engineering.
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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".