Low Cycle Fatigue of Class G Well Cement
Bibliographic record
Abstract
This paper aims to investigate the low cycle fatigue behavior of Class G well cements. While fatigue is well described for metals, wellbore cement fatigue is a rather unknown field. As well cements can be exposed to cyclic loadings situations like in enhanced oil recovery by steam injection or geothermal applications cement damage by fatigue becomes a more important issue. In order to evaluate the behavior of this material, experiments were performed to investigate how cement reacts to cyclic loadings. A low number of cycles are referred to as up to 100 loadings sequences. Class G cement was chosen as being one of the most important cement types in Germany. Samples consisting of a pipe cement compound were tested by loading them cyclic with a hydraulic press. Their failure was analyzed by determining the stress distribution inside of the samples in analytical and numerical way. It has been found that fatigue of cement is rather similar for metal and cement at least in the low cycle range. For metals there is a specific stress limit where failure can occur within several cycles. This means if the limit is exceeded the material will fail, maybe not at the first cycle, but it will fail over the cycles. Cement shows this behavior similar to metals, no other fatigue mechanisms like damage accumulation were observed, just a straight load limit. Key words: Well cement; Low cycle fatigue; Class G; Experiment
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".