Efficient Fluid-Fluid Displacement of Yield Stress Fluids in Axially Rotating Pipes
Bibliographic record
Abstract
Abstract Oil and gas well primary cementing operations involve pumping a sequence of fluids into the well, for example, cement along a circular pipe (casing) to remove (displace) in situ drilling mud. Cementing is vital to the implementation of zonal isolation and well integrity in the completion of oil and gas wells. The success of a cementing operation is largely determined by the displacement efficiency. There are several factors, such as rheological properties of fluids, geometrical specifications of the annulus, flow rate, and pipe movement, which can considerably affect the displacement efficiency. A casing rotation is generally believed to improve the displacement process, but without solid laboratory experiments to prove that such rotation is indeed effective. In this work, the influence of a pipe rotation on a displacement flow which consists of a yield stress displaced fluid is analyzed via experimental methods. A heavy Newtonian fluid (salt water) displaces a light viscoplastic fluid (Carbopol gel) in a long, inclined pipe. Our results show that the pipe rotation helps break up the Carbopol gel remained on the surface of the flow geometry, and eventually leads to an efficient removal of the displaced fluid above a critical rotation speed. The analysis includes measuring the propagation velocity of the leading front (V̂f) for different parameters, such as the pipe inclination angle, the imposed flow velocity (V̂0) and the rotation speed. The leading front velocity decreases as the rotation speed increases and it is found V̂f ≈ 1.6V̂0. Three flow regimes are observed: slumping type, ripped type and effective-removal type.
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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 | 0.001 |
| 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".