Pipeline integrity assessment using mass inspection data

腐蚀对几乎所有管道来说都是一种时间依赖性危害。由于腐蚀引起的管道失效会对社会、经济和环境造成严重后果。管道的在线检测（ILI）用于确定管道中腐蚀缺陷的尺寸，随后对测量的缺陷进行极限状态评估，以识别安全关键缺陷。ILI通常会产生大量数据，特别是对于遭受高密度内部腐蚀的管道。基于风险和可靠性的管道完整性管理在过去的几十年中获得了越来越多的相关性。它依赖于概率模型来推断实际的腐蚀增长的ILI结果，是受检查的不确定性。当前的概率腐蚀增长模型要么只关注可用检查数据的子集，要么没有被设计成有效地处理大量检查数据。因此，管道完整性评估和决策可能会由于所需的数据截断而产生偏差，从而导致次优结论。本研究计划的目标是开发基于大量ILI数据的腐蚀能源管道完整性评估的有效概率模型。提出了三个研究任务，将导致两个腐蚀生长模型，一个不匹配的缺陷和匹配的缺陷。

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.