Bibliographic record
Abstract
Abstract Plugging is an essential part of decommissioning wells. Often cement plugs are set at various depths to isolate critical zones of interest. These zones can include production zones, aquifers and surfaces, ensuring the wellbore is isolated correctly. The cement plug should hydrate and form an impenetrable barrier between the subsurface and the surface. Since the cement slurry, containing a yield stress, is typically denser than the fluid below, there is a tendency to destabilize mechanically. Proper selection of cement properties, namely the yield stress, is therefore essential to the success of the abandonment process. If the cement does not set correctly, the well integrity is compromised and another cement plug will need to be placed. This motivates the study of this paper, we study the interface between the cement and lighter Newtonian fluid. Over the initial setting time, the interface can become unstable allowing light fluid to propagate upwards into the denser fluid. Experimental studies conducted with water under a denser yield stress fluid show that the interface usually takes the form of a long finger moving centrally upwards. If this finger can reach a critical height in the cement plug before it sets sufficiently, the plug will lose its integrity and fail. Therefore, being able to predict the velocity of the finger is of critical importance. We scale our experimental setup to give an accurate representation of a typical western Canadian well. We can then derive an analytical expression for the flow rate and mean velocity of both the viscous finger and the dense fluid flowing down. We then explore this finger propagation by varying the critical parameters of the heavier fluid, namely the density and yeild stress. Experiments show that the model accurately predicts the velocity of the finger for a range of rheological properties and densities. The speed of the finger is found to be governed by the yield stress, the density contrast, and the ratio of effective viscosities.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".