系泊
诚信管理
风险分析(工程)
管理制度
风险管理
过程(计算)
计算机科学
结构完整性
系统工程
系统生命周期
工程类
可靠性工程
应用程序生命周期管理
运营管理
海洋工程
业务
机械工程
结构工程
操作系统
管道运输
程序设计语言
软件
财务
作者
Andrew Kilner,D. Washington,C. Carra,Andrew Potts
摘要
The requirement for improvements to existing practices for the management of mooring system integrity has been bought into sharp focus in recent years, with the failure rate for FPS mooring systems being significantly higher than expected. This paper will provide an overview of the recently published risk based guidelines for the management of mooring system integrity, developed by AMOG for DeepStar. The DeepStar Mooring Integrity Management (MIM) guidelines will be of interest to operators of a wide range of floating production systems, as well as to the designers of such systems. This paper will outline the key aspects of the risk based process for the management of mooring system integrity set out in the DeepStar MIM Guidelines. The integrity management framework developed in the DeepStar MIM Guidelines includes the identification of required practices at each stage of a mooring system's life cycle, in order to minimize the risk of degradation mechanisms arising from design, construction, installation and operational practices. The guidelines include two case studies to demonstrate how the model guidelines should be applied, at the design phase for a new system, and to a legacy system already in operation. In addition, the guidelines include a detailed industry survey of instances of mooring system failures or early replacement due to unanticipated degradation, which was undertaken to provide a more rigorous understanding of the failure probabilities of mooring system components. This paper presents significant contributions to the effective management of mooring system integrity, through the development of enhanced design practices that better account for through-life integrity management of moorings for new systems, and development of enhanced inspection and monitoring/prediction practices for legacy mooring systems.
科研通智能强力驱动
Strongly Powered by AbleSci AI