Risk-based analysis of squeeze cementing operations
Bibliographic record
Abstract
The success rate of a squeeze cementing operation is generally low due to complexities in the range of potential leakage pathways and enormous uncertainty in characterizing these pathways. This work aims first to develop a physically based numerical model of the key part of the squeeze cementing operation, i.e. invasion of cement slurry into a realistic microannuli geometry under operational conditions. Secondly, it aims to account for uncertainties by using statistical tools combined with the deterministic model to deliver probabilistic information regarding outcomes. This paper develops this novel risk-based approach. The first objective of this work is to build upon our previous model, which focused on a single-perforation injection scenario. We reconstruct a multi-perforation injection scenario by collocating radial (single-perforation) invasion flows. We use this model to investigate the invasion of a viscoplastic fluid, representing a cement slurry, into a randomized varying width microannulus channel. The second objective is to investigate the effect of relevant parameters on leakage reduction. This is broken into two parts. First, how effectively the squeeze operation fills the microannulus around a perforation, explored through metrics that quantify penetration/filling. Second, what effect the penetration/filling has on the leakage of the entire well, i.e. given that the squeeze operation is local. We compare the effect of different perforation patterns and rheological parameters on penetration/filling metrics and on the reduction of leakage. We generate a probability distribution of leakage rates before and after the operation. In this way can estimate both the mean reduction in leakage for different perforation patterns, and associated confidence intervals. Such predictions have not been made before. Our results demonstrate the inherent uncertainty of a squeeze operation, which arises from the geometrical complexity. Practically, this suggests that higher perforation density and lower yield stress slurry will lead to higher success rates, as is intuitive. However, the spread of uncertainty in outcomes is not reduced much by such practices, meaning that one can still be unlucky in perforating the wrong part of the annulus.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".