Despite being considered the safest means to transport oil and gas, pipelines are susceptible to degradation. Pipeline integrity management (PIM) is implemented to lower the risk of failure due to degradation and to maintain the functionality and safety of pipelines. PIM consists of a set of activities for assessing the operational conditions of pipelines. These activities generate data with high volume, velocity, and variety, due to the length of a pipeline and the number of sensors and tools used to assess the pipeline's condition. This paper provides a comprehensive review in relation to the applications of machine learning (ML) in managing and processing data generated from PIM activities. ML applications in the elements of a PIM process (e.g., inspection, monitoring, and maintenance) are investigated. The aspects of ML techniques (i.e., type of input, pre-processing, learning algorithm, output and evaluation metric) applied in each element of PIM are examined. Current research challenges and future research opportunities in the application of ML in PIM are also discussed.

尽管被认为是最安全的石油和天然气运输方式，但管道容易退化。实施管道完整性管理 (PIM) 以降低因性能下降而导致的故障风险并保持管道的功能性和安全性。PIM 由一组用于评估管道运行条件的活动组成。由于管道的长度以及用于评估管道状况的传感器和工具的数量，这些活动会生成大量、速度和多样性的数据。本文全面回顾了机器学习 (ML) 在管理和处理 PIM 活动生成的数据方面的应用。研究了 PIM 过程元素（例如，检查、监控和维护）中的 ML 应用。ML 技术的方面（即，PIM 的每个元素中应用的输入类型、预处理、学习算法、输出和评估指标）。还讨论了 ML 在 PIM 中应用的当前研究挑战和未来研究机会。