Autonomous data analytics for pipeline leakage detection

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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.