3D Simulation of Surface Casing Cementing: Dispersion Effects
Bibliographic record
Abstract
Abstract Many numerical studies have been conducted regarding laminar miscible displacement flow in narrow, vertical, eccentric annuli. For the next decade it is likely that primary cementing flows on the scale of the well will continue to be simulated predominantly with 2D gap-averaged (2DGA) models. However, 3D simulations are less common due to the computational cost. The comparison between 2D and 3D models needs further attention, to understand the main discrepancies and thus help to understand primary cementing flows better. In this paper, comparisons of 3D against 2DGA model results show a range of interesting different phenomena, e.g. static layers, dispersive spikes, and instabilities. The predictions of the 2DGA model are the same as the 3D results to a degree. In particular, they are consistent with each other regarding the evolving process, interface shape, etc. However, the main difference with the 2DGA concentration arises from dispersion on the scale of the annular gap. From the recent research of Renteria and Frigaard (J. Fluid Mech., vol. 905, 2020) [1], a variety of dispersive effects are the main discrepancy between experiments and 2DGA results as well. We give representative examples of these flows in surface casing geometries and suggest methods for improvement of the 2DGA model.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".