Experimental Study Of Viscoplastic Fluid Placement In A Confined Geometry 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 performed when a well reaches the end of its lifetime. Such P&A operations usually consist of placing several cement plugs in the wellbore to isolate the reservoir and avoid the contamination of fresh water resources, soil, and atmosphere. An economical method to place a cement plug in the wellbore is through the dump bailing method, in which a relatively small amount of cement slurry is placed inside the well on a barrier. In this method, a wide range of Newtonian or non-Newtonian fluids is used to displace and remove the in-place fluid (water) in the wellbore. We focus on an industrially interesting case where the placed fluid exhibits non-Newtonian behaviours, mainly with a significant yield stress. Motivated by the fluid mechanics of this process, we experimentally investigate the placement of a viscoplastic fluid in a vertical pipe to replace a lighter Newtonian fluid. The fluids of our interest are miscible. The concentration profiles are analyzed by image processing techniques to shed light on the mixing between the fluids in the flow domain. We investigate the effects of the viscosity ratio between the fluids, and the rheological parameters on the placement flow patterns. We successfully classify the different flow regimes that describe the flow dynamics versus the governing dimensionless numbers. We observe three distinct dynamical regimes: a break-up regime, a coiling regime, and a buckling regime. The results of this work can be used for improving the cement plug placement in P&A of the oil and gas wells.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 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".