Understanding the Role of Buoyancy on Jet Flows Within Viscoplastic Medium: Insights for Plug and Abandonment of Wells
Bibliographic record
Abstract
Abstract Plug and abandonment (P&A) operation refers to the final stage of the oil and gas well’s operational life-cycle. The successful implementation of a P&A operation ensures the secure sealing of the well to prevent fluid movement and eliminate leakage between different layers. The key steps of a P&A operation entail three processes: (1) accessing the annulus section, (2) cleaning the target area (i.e., inside and outside the casing), and (3) installing cement plug barriers. The proper cleaning helps to enhance the cement-casing bonding while mitigating the risk of cement contamination. Jet cleaning, known as an efficient technique in the second step of P&A operations, displaces undesirable fluids and materials by injecting a cleaning fluid into the target area. Fluid properties, including density difference between injected and ambient fluids and the fluids’ rheological parameters, stand as key variables that directly influence the cleaning efficiency. Hence, a comprehensive understanding of how these parameters affect the jet flow is crucial for optimizing efficiency in well-cleaning procedures. In this study, we experimentally examine the miscible jet flow dynamics for two distinct scenarios: positively buoyant jets, where momentum and buoyancy act in the same direction, and negatively buoyant jets, where they act in opposite directions. The jet flow is formed by introducing a Newtonian fluid vertically downward, through a circular nozzle, into a transparent tank containing a viscoplastic fluid. We assume a “free jet” condition in our experiments, as the large tank dimensions render the wall effect negligible on the jet behavior. We use high-speed imaging to study the effects of the injection velocity, the density difference, and the rheological parameters (in particular, the yield stress) on the jet flow dynamics. The penetration length (indicating the evolution of the jet length over time) is the primary jet feature addressed in this study, which is investigated in both positively and negatively buoyant jets. Our findings show that the yield stress of the ambient fluid resists the jet evolution, leading to a decrease in the jet penetration length.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".