Bibliographic record
Abstract
Many hydrocarbon wells leak gas, due to shrinkage and other microannuli that typically form along the cement-casing and cement-formation interfaces. These microannuli are variable due to irregularities in the primary cementing process and other operational anomalies. Repair of such defects is via a process called squeeze cementing, that involves pumping a thin cement slurry into the microannulus under pressure. Trudel & Frigaard1 developed a stochastic model of well leakage able to predict all but extreme (high and low) rates of leakage for a median well in British Columbia (BC), Canada, benchmarked against leakage rates observed in 2010-2019. Izadi et al.2,3 have explored the effects of pumping (yield stress) slurries into these narrow irregular geometries, using a Monte-Carlo approach to account for the extreme variability. This enabled us to give probabilistic predictions of the likely effects of the squeeze cementing operation. Here we extend our analysis to different operational scenarios, showing how rheological effects can influence repair of the microannulus.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".