Reducing Environmental Risk with Siemens Energy's Spontaneous Leak Detection As-a-service, powered by ProFlex Technologies.

The midstream industry continues to embrace data driven solutions. Leveraging edge computing, new algorithms, and cloud computing to significantly enhance the ability to quickly identify spontaneous leaks in legacy pipelines is critical for midstream operations.  In addition, this IoT-based Spontaneous Leak Detection can protect legacy assets from risk dialogue in pipelines, gathering systems, and offshore risers.

With IoT leak detection, spontaneous leaks can be detected in seconds and localized to +/-20 feet and leaks as small as 0.17% of the pipeline cross section can be detected. Integration of spontaneous leak detection alerts into existing SCADA and other corporate systems increases response and mitigation times. With this comes continuing improvements in sensors, computing power, communications technology, and improved/newly designed algorithms to detect the negative pressure wave from the background pipeline “noise.” The use of easy to deploy advanced hardware technology insist that pipeline leak detection can be easily achieved on brownfield as well as greenfield projects. 

This is made possible by analyzing pipeline behavior and developing bespoke approach to signal filtering for each individual pipeline and by continuously updating algorithms according to changes in pipeline flow parameters. By combining the IoT-based spontaneous leak detection technology with a “as a Service” business model, there is minimal impact on the midstream operations and especially the staff. Midstream staff do not have to learn and maintain a new system. Since this supplied as a Service, there is no CapEx investment up front.

We will discuss the novel approach to pipeline signal noise reduction and real-time adjustment to pressure wave propagation rates to improve the accuracy of leak location and minimizing false positives which are both key to implementing a successful pipeline leak detection system.