用在役和水压试验失效验证裂缝评估模型

Jason Yan, Shenwei Zhang, S. Kariyawasam, Maria Pino, Taojun Liu
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引用次数: 4

摘要

裂缝或类裂纹异常是威胁油气管道安全和结构完整性的主要问题之一。在管道工业中,人们开发了各种各样的评估模型来预测含有纵向表面裂缝的管道的爆裂能力。这些模型具有不同程度的保守性、准确性和精密度,这对管道运营商的限压、开挖和修复等完整性缓解决策产生了重大影响,也导致了不同程度的安全性。本文利用在役和静压试验失效数据,比较了最常用的裂纹评估模型(即Modified Ln-Sec、CorLAS、API 579 Level 2)和最近发布的PRCI MAT-8模型的准确性和精密度。收集了12个在役管道和63个静压试验管道因应力腐蚀开裂(SCC)而破裂的实际破裂压力、材料性能和详细的裂纹尺寸测量数据,并用于推导与每个模型相关的模型误差的概率特征。与实验室爆破试验和其他研究相比,在役和静压试验中获得的结果更能代表管道运行中的真实边界条件。详细讨论了与每个模型相关的所有假设和经验相关性。分析结果表明,CorLAS是最准确的模型,不确定性最小(或精度最高)。通过使用最准确和精确的模型,可以在不影响安全的情况下优化缓解活动。
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Validate Crack Assessment Models With In-Service and Hydrotest Failures
Crack or crack-like anomaly is one of the major threats to the safety and structural integrity of oil and gas pipelines. Various assessment models have been developed and used within pipeline industry to predict the burst capacity for pipelines containing longitudinally-oriented surface cracks. These models have different level of conservatism, accuracy, and precision which significantly impacts pipeline operators’ integrity mitigation decisions such as pressure restriction, excavation, and repair, and also lead to different level of safety. This paper compares the accuracy and precision of the most commonly used crack assessment models, i.e. Modified Ln-Sec, CorLAS, API 579 Level 2 and the recent-published PRCI MAT-8 model using in-service and hydrostatic testing failure data. A total number of 12 in-service and 63 hydrostatic test pipe ruptures due to stress corrosion cracking (SCC) with actual burst pressure, material property, and detailed crack size measurements are collected, and used to derive the probabilistic characteristics of the model errors associated with each model. Compared to the burst tests conducted in the laboratory and investigated in other previous studies, the results obtained from in-service and hydrostatic test ruptures are more representative of the real boundary conditions in pipeline operation. All the assumptions and empirical correlations associated with each model are discussed in details. The analysis result suggests that CorLAS is the most accurate model with the least uncertainty (or highest precision). Mitigation activities can be optimized without compromising safety by using the most accurate and precise model.
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