Pipeline integrity assessment using mass inspection data

对于几乎所有的管道来说，腐蚀都是一种随时间变化的危险。管道腐蚀失效会给社会、经济和环境带来严重后果。管道在线检测(ILI)被用来确定管道中腐蚀缺陷的大小，随后对所测量的缺陷进行极限状态评估以识别安全关键缺陷。ILI常常导致海量数据，特别是对于易受高密度内腐蚀影响的管道。基于风险和可靠性的管道完整性管理在过去几十年中获得了越来越多的相关性。它依靠概率模型来从ILI结果中推断实际的腐蚀增长，这些结果受到检查不确定性的影响。当前的概率腐蚀增长模型要么只关注可用检测数据的一个子集，要么不能有效地处理大量检测数据。因此，管道完整性评估和决策可能会因为所需的数据截断而变得有偏差，从而导致次优结论。本研究的目的是基于海量ILI数据，为腐蚀能源管道的完整性评估开发有效的概率模型。提出了三项研究任务，将产生两种腐蚀增长模型，一种针对非匹配缺陷，另一种针对匹配缺陷。 在第一个任务中，使用基于总体的方法建立了不匹配缺陷的概率腐蚀增长模型。将来自ILI的所有测量的缺陷大小视为一个总体，通过对大小、可检测性和错误调用不确定性进行调整来确定每个ILI的实际缺陷大小的总体。然后，为整个管线段的完整性评估推断当前和未来的缺陷大小。 第二个任务是从海量ILI数据中开发腐蚀缺陷的概率匹配算法。由于测量误差、高密度腐蚀和新缺陷的生长，来自两个或更多ILI的报告缺陷位置很少完全匹配。现有的方法，如迭代最近点法，都是从计算机视觉领域采用的。它们根据错误调用数量的增加以及以前检查中的几个缺陷在一个缺陷中一起增长的可能性进行了调整。研究了一种概率模型来对描述匹配不确定性的可能的缺陷组合进行排序。 在第三个任务中，建立了匹配缺陷的分层腐蚀增长模型。除了现有的尺寸误差调整能力和模拟实际缺陷增长的随机过程之外，该分层模型还包括已开发的匹配不确定性。将其推广到轴向、周向和径向的缺陷生长分析，研究了一种估计未知随机变量的有效方法。

Corrosion is a time-dependent hazard for virtually all pipelines. Pipeline failures due to corrosion can lead to severe consequences for society, the economy and environment. In-line inspections (ILIs) of pipelines are used to size corrosion defects in pipelines and subsequent limit state assessment on the measured defects is performed to identify safety-critical defects. ILIs often lead to mass data, particularly for pipelines that are subject to high density internal corrosion. Risk- and reliability-based pipeline integrity management has gained increasing relevance over the last decades. It relies on probabilistic models to infer the actual corrosion growth from the ILI results that are subject to inspection uncertainties. Current probabilistic corrosion growth models either focus only on a subset of the available inspection data or are not designed to efficiently process mass inspection data. As a consequence, pipeline integrity assessment and decision making can become biased due to required data truncation leading to sub-optimal conclusions. The objective of this research program is to develop efficient probabilistic models for the integrity assessment of corroded energy pipelines based on mass ILI data. Three research tasks are proposed that will result in two corrosion growth models, one for unmatched defects and one for matched defects. A probabilistic corrosion growth model for unmatched defects is developed in the first task using a population-based approach. Considering all measured defect sizes from an ILI as one population, the population of actual defect sizes is determined per ILI by making adjustments for sizing, detectability and false call uncertainties. The current and future defect sizes are then inferred for the integrity assessment of entire pipeline segments. The second task focuses on the development of a probabilistic matching algorithms for corrosion defects from mass ILI data. Reported defect locations from two or more ILIs rarely match exactly due to measurement errors, high-density corrosion and growth of new defects. Existing methods such as iterated-closest-point method are adopted from the field of computer visions. They are adjusted for the increased number of false calls and the possibility that several defects from previous inspections grew together in one defect. A probabilistic model is investigated to rank the possible defect combinations for describing the matching uncertainties. A hierarchical corrosion growth model for matched defects is developed in the third task. In addition to the existing capabilities of sizing error adjustment and a stochastic process to model the actual defect growth, the hierarchical model includes the developed matching uncertainties. It is extended to analyze the defect growth in axial, circumferential, and radial direction and an efficient approach to estimate the unknown random variables is investigated.