Plug and Abandonment of Oil and Gas Wells: Experimental Study of Suspension Fluid Placements in a Confined Geometry With Insights Into the Dump Bailing Method
Bibliographic record
Abstract
Abstract Plug and abandonment (P&A) of oil and gas wells is a promising technique for mitigating greenhouse gas emissions, groundwater contamination, and ecological damage caused by reservoir fluid leakages from wells at the end of their lifespan. In this operation, precise cement plug placement is crucial at specific intervals within the wellbore to achieve highest cement placement efficiency, while minimizing mixing between the wellbore fluid and cement. Several techniques are used for cement plug placement, among which the dump-bailing method stands out as a ringless, fast, and cost-effective approach widely used worldwide. This method involves the injection of cement slurry from a bailer and allowing it to settle on top of a permanent bridge plug. Fluid flow dynamics during this process is governed by several critical parameters, including the properties of the in-situ and injected fluids, geometric parameters, and operational conditions. In this study, we examine injection of heavy suspension fluid (representative of cement slurry) into a near-vertical closed-end pipe filled with a light Newtonian fluid (representative of wellbore fluid) through a scaled-down experimental setup. We focus on the impact of the suspension injection rate on placement efficiency. By utilizing high-speed camera imaging, detailed flow dynamics is captured. Our experimental results indicate that an increasing injection rate improves suspension placement within the pipe. The findings of this study can help us better understand the fluid dynamics involved in cementing processes using the dump-bailing method, as well as the importance of injection rate as a primary operational parameter when cement slurry is considered as a suspension.
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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 | 0.001 |
| 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".