Fluid Placement in a Closed-end Pipe with Application in the Plug and Abandonment of Oil and Gas Wells
Bibliographic record
Abstract
The plug and abandonment (P&A) of oil and gas wells is a crucial process to prevent the migration of the reservoir fluids and avoid the contamination of fresh water resources, soil, and atmosphere. In order to plug and abandon a well, the cement plug placement is conducted via several methods, such as the dump-bailing method, i.e. dumping of the cement slurry into an in-situ fluid in the wellbore, at specific intervals. In this process, an extensive range of Newtonian or non-Newtonian fluids is used to displace and remove the in-situ fluid (drilling fluid or water) in the wellbore. Based on the large number of parameters of the flow, such as the density and viscosity differences between the fluids, the geometry type (pipe, annulus, etc.), the operation conditions (velocity, geometry inclination, dumping height), various kinds of placement and mixing flows can occur, and different flow regimes (e.g. inertial, viscous) can develop. Motivated by the fluids mechanics of this process, we experimentally investigate the placement of a fluid in an inclined closed-end pipe to replace a slightly lighter fluid. In our experiments, the heavy fluid can be Newtonian or viscoplastic, and the light fluid is always Newtonian. The fluids of our interest are miscible. We investigate the effects of the flow parameters, such as the density difference, the indication angle, the viscosity ratio between the fluids, and the rheological parameters on the placement flow patterns, and quantify the different flow regimes versus the appropriate dimensionless groups that describe the flow.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".