A practical framework for modeling flow in uncertain geometries
Bibliographic record
Abstract
Gas leakage in hydrocarbon wells often arises due to shrinkage and the formation of microannuli along the cement-casing and cement-formation interfaces. These microannuli develop from inconsistencies during primary cementing and operational challenges, ultimately compromising well integrity. Squeeze cementing is a widely used remediation technique in which a pressurized cement slurry is injected into these defects to restore zonal isolation. However, its success rate remains relatively low due to significant uncertainties in microannulus geometry, slurry behavior, and evaluation methods.Our proposed framework builds upon the stochastic leakage model developed by Trudel and Frigaard, which predicts potential leakage pathways based on field data from British Columbia, Canada. We extend this approach by incorporating a physical model to describe the invasion of viscoplastic slurries into these pathways. A Monte Carlo method is employed to simulate the injection of yield-stress slurries into irregular microannuli, providing probabilistic predictions of squeeze cementing outcomes and highlighting the inherent variability in the process.This study explores various operational scenarios, emphasizing the impact of slurry rheology and other key factors influencing microannulus repair. By integrating probabilistic modeling with a detailed analysis of fluid behavior, we aim to reduce uncertainties, enhance the success rate of squeeze cementing, and propose practical strategies for achieving effective zonal isolation and mitigating gas migration in hydrocarbon wells.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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; both teacher heads agree on what is shown here.
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".