Autonomous data analytics for pipeline leakage detection

原油管道泄漏检测是防止污染、减少环境破坏的重要手段。此外，它可以降低与维修和石油损失相关的高成本。原油管道泄漏通常可以通过测量压力信号来检测。与TOKU Industry Inc.合作一家位于阿尔伯塔的专门从事石油和天然气管道监测系统设计的公司，我们建议开发一种基于更鲁棒和准确的泄漏模式识别的自主泄漏检测方案。该方案包括一个板载泄漏检测算法，它能够产生实时报警，和离线压力数据模型，用于提取泄漏模式和趋势，随着时间的推移，他们的变化。设计的主要考虑因素包括通过减少假警报和漏警报的数量来提高检测精度，以及在便携式智能压力传感器上实现的板载检测算法的低计算复杂度。在这项工作中，我们专注于开发可以轻松部署的具有成本效益的软件解决方案。我们建议通过探索大量的历史数据集来构建泄漏特征的数据模型，而不是基于流体力学和实验来构建瞬态模型。

Leakage detection in crude oil pipelines is extremely crucial because it can help prevent pollution and reduce damage to environment. Furthermore it can reduce high cost related to repairs and loss of oil. Leakage in crude oil pipelines can often be detected by measuring pressure signals. Working with TOKU Industry Inc. a company based in Alberta specializing in design of monitoring systems for Oil&Gas pipelines, we propose to develop an autonomous leakage detection scheme based on more robust and accurate leak pattern identification. The scheme consists of an onboard leak detection algorithm which is capable of generating real-time alarms, and an off-line pressure data model used to extract leakage patterns and trend their change over time. Main considerations of the design include improved detection accuracy by reducing the number of false alarms and missed alarms, as well as low computational complexity for the onboard detection algorithm to be implemented on the portable smart pressure sensors. In this work, we focus on developing cost-effective software solutions that can be easily deployed. Instead of constructing transient models based on hydrodynamics and experiments, we propose to construct a data model for leak signatures by exploring a great number of historical datasets.