Bibliographic record
Abstract
Abstract In Canada, the Alberta Energy Regulator’s (AER) liability report, issued in 2018, predicted that the number of inactive wells in the province will double by 2030. Despite the increase in the number of inactive wells, there is a need to close them properly to avoid hazards escape. Various aspects of well plug technologies in the Canadian abandoning industry are empirical. Many plugs are formed by injecting cement slurry into wells that are otherwise filled with fresh water for the slurry to build up on top of a water layer at a desired location. However, cement is heavier than water. Thus, successful plug placement following this methodology is questionable from the hydrodynamics perspective. The present study aims to identify features of successful processes for placement of off-bottom plugs. To that end, we investigate mixing of fluids of different densities as the denser fluid is injected into the lighter fluid. Cement slurry is modeled as a viscoplastic fluid. The fluid motion is governed by hydrodynamic models, and the two fluids (i.e. cement and water) are considered to be miscible and are allowed to mix. Systematic numerical simulations aim to reveal how the characteristics of cement and the well configuration affect the placement process. We show that successful plug placement depends on the formation of a mixed layer, of the wellbore fluid and cement slurry, below the injection site. We identify and provide representative cases of the processes promoting the formation of the mixed layer: high diffusion and growing instabilities.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".