Magnetic flux leakage in-line inspection data analysis and integration

漏磁在线检测数据分析与集成

• 批准号: 515074-2017
• 负责人: Liu, Zheng
• 金额: $ 2.43万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development Grants
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=618022
• 关键词: Magnetic; flux; leakage; line; inspection

The non-destructive inspection of oil & gas pipelines is essential for pipeline integrity management and criticalfor environmental protection from pipeline leakage and failure. The in-line inspection (ILI) with magnetic fluxleakage (MFL) method is a key technology. The use of differently configured inspection systems, such as axialMFL and circumferential MFL, can offer a comprehensive assessment of pipeline conditions. However, theuncertainty associated with inspection results remains a challenge for maintenance decision making due to theinherent heterogeneities with varied inspection techniques.This research project is to develop machine learning algorithms to identify the consistency between the ILI databy exploring the common feature patterns from multiple inspections. A comprehensive evaluation with leastuncertainty can be achieved by integrating multi-modal in-line inspection data. Thus, this research will enableefficient pipeline integrity management for pipeline operation. The research outcomes will be in the form ofsoftware tools, which can be delivered to the industrial partner for commercializing and better serving theneeds of Canada's oil & gas industry.

油气管道无损检测是管道完整性管理的基础，也是防止管道泄漏和失效对环境保护的关键。漏磁在线检测技术是一项关键技术。使用不同配置的检测系统，例如轴向漏磁和周向漏磁，可以提供对管道状况的全面评估。然而，检测结果的不确定性仍然是维修决策的一个挑战，因为不同的检测技术具有内在的异构性。本研究旨在开发机器学习算法，通过从多个检测中发现共同的特征模式来识别ILI数据之间的一致性。通过集成多通道在线检测数据，可以实现具有最小不确定度的综合评价。因此，本研究将为管道运行提供有效的管道完整性管理。研究成果将以软件工具的形式提供给工业合作伙伴，以便将其商业化并更好地服务于加拿大石油和天然气行业的需求。