Analyzing the Fluid Mechanics of the Dump Bailing Method in the Plug and Abandonment of Oil and Gas Wells
Bibliographic record
Abstract
Abstract Plug and abandonment (P&A) of oil and gas wells is receiving an increased attention. The P&A operation is performed by placing a barrier, such as a cement plug to avoid reservoir fluids migration toward aquifers. To fulfill these requirements, the desired cement plug should be placed in the wellbore with minimum mixing with the in-situ fluid. A rigless way for placing cement slurry in the wellbore is through the dump bailing method, in which a relatively small amount of cement slurry is injected on a mechanical barrier inside the well to replace the in-situ wellbore fluids (mostly fresh water). The dynamics of the fluid placement is governed by several parameters, such as the flow and geometry parameters, and the fluid properties. In this study, we analyze the fluid mechanics of the dump bailing method, via experimentally investigating the effects of the viscosity ratio between the replacing and replaced fluids in the process. The viscosities of the fluids involved have significant effects on the mixing and placement flow quality. In our experiments, the fluid placement is carried out in a near-vertical closed-end pipe (i.e. representative of the well casing) to replace an in-situ light fluid. The two fluids are considered to be miscible, and they have a fixed density difference. Our results show that the most efficient placement happens with the injection of the higher viscous fluid. The outcomes of this study can be used for improving the cementing processes in the dump-bailing method of P&A operations.
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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 | 0.000 |
| 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".