High fidelity distributed sensing (HDS) provides high fidelity integrated acoustics, temperature and strain/vibration, and is optimized to do so over long distances. This makes the HDS technology particularly appropriate for preventative pipeline leak detection as well as a number of other value-added applications which can leverage acoustics and either real time or cumulative strain/vibration measurements. The cumulative strain measurements can be extremely valuable from a pipeline integrity perspective in terms of monitoring applications such as slope stability, ground subsidence, and other points of measured strain including a variety of pigging-related activities.
A recent novel application involved collaborating with a major producer (Suncor Energy) for the successful in-situ remediation of ovality issues via monitoring of pig-induced strain signatures captured by HDS, with segments of the pipe constructed via directional drills. Such measurements bring direct operational savings, while also assisting the pipeline operator in understanding where the dynamic / elastic segments exist along the right-of-way and detecting strain anomalies in a spatial or temporal context.
Case studies will be provided to showcase the value of using supervised / unsupervised machine learning and high-fidelity distributed sensing to enable ground disturbance / security intrusions, geotechnical monitoring of earthquakes and slope movement, pig detection / real time pig tracking, as well as analysis of multiple pig runs. A specific case study will be provided to demonstrate how the cumulative strain analysis provided by the HDS technology contributed to the identification of the geohazard risks to a pipeline, leading to the ultimate decommissioning of the pipe. Case studies demonstrating the value of tracking the timing and location of pig stoppages for integrity monitoring will also be presented. Other “value added” applications such as flow monitoring of anomaly detection, flow rate, pressure, and density estimation will also be presented.