Pipeline integrity assessment using mass inspection data

腐蚀对几乎所有的管道都是一种随时间变化的危害。管道腐蚀引起的故障会对社会、经济和环境造成严重后果。管道在线检测（ILIs）用于确定管道腐蚀缺陷的尺寸，随后对测量缺陷进行极限状态评估，以识别安全关键缺陷。ili通常会导致大量数据，特别是对于受高密度内部腐蚀影响的管道。在过去的几十年里，基于风险和可靠性的管道完整性管理得到了越来越多的关注。它依靠概率模型从ILI结果中推断实际腐蚀增长，这些结果受检测不确定性的影响。目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，由于所需的数据截断导致次优结论，管道完整性评估和决策可能会产生偏差。本研究计划的目标是基于大量ILI数据开发有效的概率模型，用于腐蚀能源管道的完整性评估。提出了三个研究任务，将产生两种腐蚀生长模型，一种是针对不匹配缺陷的，另一种是针对匹配缺陷的。

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.