Evaluating the effectiveness of a CPM implementation via leak testing is paramount to confirm that the performance of the CPM system is acceptable based on a pipeline company's risk profile for detecting leaks. However, leak testing of a CPM system is challenging due to the complexity of the CPM design, as well as the need to stress test the CPM over the breadth of operational scenarios to assess the robustness of the CPM, where test coverage includes steady state threshold sensitivity, transient threshold sensitivity and the threshold switching action.
This paper reviews the leak testing challenges encountered during CPM implementation and evaluation, outlines its limitations, and proposes a novel approach to an API RP 1130 recommended test method that can be applied to stress test CPM sensitivity, providing an evaluation of CPM robustness over a range of varying operating scenarios. The concept of the new testing methodology, along with a feasibility study on the automation of the test process, is discussed. Extensive tests are carried out to evaluate and assess the new testing methodology, and a comparison is made with other API RP 1130 recommended leak test methodologies such as parameter manipulation tests, simulated leak tests, and fluid withdrawal tests. The results indicate that the proposed technique has far wider testing coverage compared to existing approaches to leak testing while providing similar sensitivity measurement results and appears promising for use in stress testing sensitivity of CPM systems to gain an understanding of CPM robustness, which in turn has improved the sensitivity and robustness of Enbridge's current leak detection systems.
In the operation of hydrocarbon liquid pipelines, Computational Pipeline Monitoring (CPM) systems are either mandated by legislation (e.g. in Alberta) or must meet regulatory requirements when they are installed (e.g. the current situation in United States). Software-based algorithmic monitoring tools can be used to enhance the abilities of a Pipeline Controller to recognize hydraulic anomalies in the operations of a pipeline, such as a leak or commodity release.