Development of a successful untethered leak detection and pipe wall condition assessment technology for large diameter pipelines

Annual Conference

Leak detection in large diameter transmission water pipelines has historically proven difficult for operators.

Traditional leak correlators have worked well on smaller diameter distribution networks but have limitations in detecting leaks on larger diameter pipe (> 300 mm). In many cases, large diameter transmission mains are the backbone to most utility systems and, as a result, have a high level of consequence of failure. Therefore, identifying leaks on a these large diameter pipelines can be of greater value when looking at the overall risk of failure.

Technologies that detect acoustic activity associated with leaks offer an inspection technique for transmission main assessments to occur during full operation of the pipeline. Identification of leaks along the pipeline allows the system owner to find and repair defects that are a source of non-revenue or, in some cases, can cause harmful environmental impacts.

The SmartBallĀ® acoustical inspection technology allows for a unique, un-tethered inspection of many kilometers of pipeline during a single deployment. The technology is inserted into an operational pipeline and travels with the flow for up to 15 hours collecting the time and location of leaks as it traverses the pipeline. Once extracted from the pipeline, the data can be downloaded to a laptop computer to evaluate the number, size and location of the leaks. To date, the technology has been successfully deployed on over 2,000 km of water pipelines.

In more recent times the technology has been adapted to also complete pipe wall condition assessment by emitting a low frequency resonance into the pipeline.

This paper will discuss the challenges and solutions employed in order to successfully deploy, retrieve, and analyze data for an untethered device in pipelines for several utilities.

Conference Papers Distribution and Infrastructure Resource - Conference Papers

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29 Jun 2016