Placing Off-Bottom Cement Plugs: The Influence of Contamination From Sub-Optimal Placement in Vertical Wells
Bibliographic record
Abstract
Abstract In Canada, a common approach in well abandonment is the placement of cement plugs using the balanced plug method. This involves the injection of cement slurry through a concentric injector into a wellbore typically filled with wellbore fluid. We use numerical simulations to examine the effect of contamination of the cement slurry on the dynamics of the plug placement in vertical wells. The slurry is characterized across varying contamination levels using API RP 10B-2 standards, and its rheology is modeled using three models: (i) Bn Linear, where rheological properties follow a linear variation with contamination based on the Bingham model; (ii) HB Linear, which uses the Herschel-Bulkley model with linear rheological variations; and (iii) HB Exponential, which employs the Herschel-Bulkley model with exponential rheological variations. Results show that slurry rheology varies exponentially with contamination level. Rapid decrease in the yield stress and consistency of the contaminated slurry expedites the onset of shear instabilities and changes the mixing layer characteristics below injector. HB exponential model predicts less particle sedimentation compared to other models, which improves the plug placement. Overall, we highlight the influence of rheology in modelling cement plug placement processes, a key contributor to the success of well abandonment operations.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".