Fit-for-Service Assessment of Deepwater In-Service Low Toughness Mooring Shackles
Bibliographic record
Abstract
Abstract Due to a recent mooring shackle failure within the offshore industry, focus has been directed toward existing in-service facilities that have similar manufactured shackles installed in the mooring systems. An in-service deep water facility was identified as having similar mooring components by the same manufacturer that failed in-service. A subsequent assessment of the material properties was undertaken on readily available spare shackles that identified Charpy impact toughness values below the original material specification. Consequently, a fitness-for-service assessment was carried out that included the assessment of the shackle fracture toughness, a finite element analysis of the shackle and associated mooring components, and a deterministic analysis procedure that assessed the structural capacity of the shackle by considering the response of the shackle to the presence of a crack or flaw located at a shackle stress hot spot coupled with a significant return period event. Finally, full-scale structural testing of spare shackles was performed to enable verification of actual shackle load capacity with a flaw located at the shackle stress hot spot. The focus of this paper is on presenting an overview of the fit-for-service methodology and the results of the structural integrity assessment. While a full quantitative risk assessment of the effect of a low fracture toughness shackle in the mooring system would have been ideal, due to the level of data available and the timeframe required for a probabilistic assessment, it was deemed appropriate to undertake a deterministic assessment of the mooring shackles under realistic scenarios representative of service and severe loads that may accompany the service. The results of the deterministic assessment on an actual in-service deepwater floating facility will be presented. The fit-for-service methodology presented in this paper provides a robust method to assess in-service floating facilities that have been identified or suspected of having below-specification mooring components. It has successfully been applied to a number of Chevron in-service floating facilities in different geographical locations. Introduction Following the early failure of a mooring shackle on a new deepwater floating facility, attention has been directed to other new and existing assets that have similar manufactured mooring shackles installed in the mooring system. Additionally, the release of Safety Alert No. 256 from the U.S. Department of the Interior (Minerals Management Service), provided recommendations to operators to review their current specification and testing requirements for mooring systems to ensure that the mooring components are adequate for the proposed or current usage.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".