Three-Dimensional Hydrodynamics of Off-Bottom Plug Placement in Eccentric Wellbores
Bibliographic record
Abstract
Abstract Successful plug and abandonment (P&A) operations in oil and gas wells are critical for environmental protection and cost-efficiency. A standard practice involves placing at least two plugs, with the upper plug (referred to as the off-bottom plug) playing a vital role in ensuring well integrity. In western Canadian practices, cement slurry is injected into wells filled with water during the initial stages of off-bottom plug placement. Ideally, the injector and wellbore are perfectly aligned, but in real-world scenarios, misalignment (eccentricity) is inevitable and can compromise plug integrity. In our previous studies, we developed a 2D model to simulate the early stages of off-bottom plug placement under eccentric conditions, revealing that water content on the narrow side of the annulus can significantly exceed target levels. Building on this foundation, the present study introduces a 3D model to provide a more comprehensive and representative analysis of the mixing dynamics between the injected slurry, modeled as a viscoplastic fluid, and the wellbore liquid, represented as a Newtonian fluid. The study investigates a range of eccentricity levels and compares the hydrodynamic behavior and water content predictions between 2D and 3D models. By delivering enhanced predictive accuracy, this work aims to optimize P&A operations, improving plug placement reliability and overall success rates.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".