Injecting A Viscoplastic Fluid In A Channel Filled With A Lower Density Newtonian Fluid: Effects Of Premixing
Bibliographic record
Abstract
Oil wells are normally abandoned using several cement plugs.Plugs above the deepest one are called off-bottom.The balanced plug method is the most used technique to place off-bottom plugs.In most cases, the initial stage of the balanced plug method includes injection of the cement slurry in cased wells.A tube smaller than the casing is inserted to the depth where the plug should be placed.Cement slurry is then injected through the tube into the cased wellbore, that is initially filled with wellbore liquids.Under field conditions, mixing of the fluids inside the injector is inevitable.In this study, we model the above cement injection process in a representative two-dimensional domain.We explore the effect of fluids premixing inside the injector on the dynamics below it.We first consider an idealized case where the displacing fluid fills the injector initially.To model the fluids premixing, we consider three other cases where the injector is initially filled with the displaced fluid, a buffer layer of both fluids and a combination of both.Our preliminary results show that the injection in the idealistic case results in a cement finger developing below the injector.The finger then breaks due to an interfacial instability.As a result, a mixed layer forms below the injector.The premixing of fluids in the injector result in qualitatively similar dynamics as above.Mixed fluids advect below the injector.Shortly after, unsteady dynamics, within the injector or below it, facilitate the formation of a mixed layer below the injector.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".