Concordia and Hong Kong Polytechnic University scientists have created new failure prediction models to enhance the safety of oil and gas pipelines.
According to the US Department of Transportation (DOT), there have been more than 10,000 pipeline failures in that country alone since 2002.
Complicating safety measures are the cost and intensity of labor required to monitor the health of the thousands of kilometers of pipelines that crisscross Canada and the U.S.
Now scientists from Concordia and the Hong Kong Polytechnic University have created new methodologies built on older models to predict and limit pipeline failure.
“In many of the existing codes and practices, the focus is on the consequences of what happens when something goes wrong,” says Concordia associate professor Fuzhan Nasiri.
“Whenever there is a failure, investigators look at the pipeline’s design criteria. But they often ignore the operational aspects and how pipelines can be maintained in order to minimize risks.”
Nasiri and his colleagues, PhD student Kimiya Zakikhani and Hong Kong Polytechnic professor Tarek Zayed, identified five failure types:
- Mechanical – the result of design, material or construction defects.
- Operational – due to errors and malfunctions.
- Natural hazard – such as earthquakes, erosion, frost or lightning.
- Third-party – damage inflicted either accidentally or intentionally by a person or group.
- Corrosion – the deterioration of the pipeline metal due to environmental effects on pipe materials and acidity of oil and gas impurities.
According to the scientists, corrosion is the most common and the most straightforward to mitigate.
Nasiri and his team found that the existing academic literature and industry practices around pipeline failures need to further evolve around available maintenance data.
They believe the massive amounts of pipeline failure data available via the DOT’s Pipeline and Hazardous Materials Safety Administration can be used in the assessment process as a complement to manual in-line inspections.
These predictive models, based on decades’ worth of data covering everything from pipeline diameter to metal thickness, pressure, average temperature change, location and timing of failure, could provide failure patterns.
They could also be used to streamline the overall safety assessment process and reduce costs significantly.
Image and content: Shutterstock/Concordia University