Development of novel apparatus to obtain soil resistance–displacement relationship for well conductor fatigue analysis
Bibliographic record
Abstract
An important aspect of deepwater well integrity is development of accurate conductor fatigue analysis due to cyclic loading during drilling operations. Fatigue damage in a structure occurs from stress changes in response to cyclic loading. In practice, the lateral cyclic soil response is typically modelled using Winkler lateral load–displacement (p–y) springs. However, recently developed soil models for conductor fatigue analysis are based on physical modelling in a geotechnical centrifuge. Notwithstanding the advantages of centrifuge modelling for investigating the conductor–soil interaction mechanism, development of simple laboratory tools to obtain p–y data directly from intact soil samples obtained from the field can also be very beneficial. This paper describes the development of a novel apparatus to obtain p–y and soil damping relationships from field samples specifically tailored for well conductor fatigue analysis. In addition, it compares test results obtained using reconstituted kaolin clay and intact natural Onsøy clay with centrifuge test results; ultimately demonstrating a satisfactory agreement between the two techniques. The results are highly encouraging and are believed to present a major step forward in deepwater well conductor fatigue analysis. The findings may also be beneficial to the offshore renewable energy sector.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".