Manuscript Title: Characterization of Microannuli at the Cement-Casing Interface: Development of Methodology
Bibliographic record
Abstract
Abstract Formation of microannuli at the interface of cement-casing can create well integrity issues. X-ray CT and Optical microscopy are technological trends that may have potential for direct visualization of microannuli. CT has an advantage of providing non-destructive visualization of microannuli, but its resolution suffers with increase in casing thickness. Conversely, Optical microscopy has the potential of providing higher resolution needed to detect smaller sized microannuli; however, information about microannuli is limited to only a few sections where samples have been sliced. The objective of the current article is to describe a methodology to examine the interface of cement-casing. Experimental work was combined with literature review. This includes both direct visualization methods, evaluation of current trends to better understand the characteristics and geometric variation of relevant leakage paths. We generate test specimens consisting of cement plugs, various steel casing thickness and nano-coated aluminium casings. Hydraulic sealability tests were conducted by injecting water at the cement-casing interface. Flow rates are then interpreted in terms of microannuli aperture and direct visualization of the cement plug-casing interface by CT and Optical microscopy was implemented. The experimental findings of this article will form a basis for studying geometry and size of microannuli as well as modelling of fluid migration.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.007 | 0.004 |
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".