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Research Team Examines Pipeline Failure Prediction Models

Engineering professor Fuzhan Nasiri led a research team examining many methodologies currently used to predict pipeline failures. Photo courtesy of Concordia University.

In a new study,1 researchers at Concordia University (Montreal, Quebec, Canada) and the Hong Kong Polytechnic University (Hung Hom, Hong Kong) are examining many methodologies currently used by industry and academics to predict pipeline failure—and their limitations.

The researchers cite more than 10,000 pipeline failures in the United States alone as the motivation for their research, according to U.S. Department of Transportation (DOT) (Washington, DC, USA) data.

Ignoring Operational Aspects

“In many of the existing codes and practices, the focus is on the consequences of what happens when something goes wrong,” says Fuzhan Nasiri, an associate professor in Concordia’s engineering department and co-author of a new research paper on the topic. “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.”

The full research paper, titled “A Review of Failure Prediction Models for Oil and Gas Pipelines,” can be read in the February 2020 edition of the Journal of Pipeline Systems Engineering and Practice.2 Nasiri, who runs the school’s Sustainable Energy and Infrastructure Systems Engineering Lab, co-authored the paper with Doctoral Student Kimiya Zakikhani and Hong Kong Polytechnic Professor Tarek Zayed.

Five Failure Types

In their work, the researchers 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, meaning damage inflicted either accidentally or intentionally by a person or group; and corrosion, the deterioration of the pipeline metal due to environmental effects on pipe materials and acidity of oil and gas impurities. This last one is the most common and most straightforward to mitigate, according to the researchers.

Nasiri and his colleagues found that 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 U.S. DOT’s Pipeline and Hazardous Materials Safety Administration (Washington, DC, USA) 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, and the location and timing of failures—could provide failure patterns. These could be used to streamline the overall safety assessment process and reduce costs significantly.

Trends and Patterns

“We can identify trends and patterns based on what has happened in the past,” Nasiri says. “And you could assume that these patterns could be followed in the future, but need certain adjustments with respect to climate and operational conditions. It would be a chance-based model: given variables, such as location and operational parameters as well as expected climatic characteristics, we could predict the overall chance of corrosion over a set time span.”

He adds that these models would ideally be consistent and industry-wide, and transferrable in the event of pipeline ownership change. In turn, he hopes research like this could influence industry practices.

“Failure prediction models developed based on reliability theory should be realistic,” Nasiri says. “Using historical data [with adjustments] gets you closer to what actually happens in reality. They can close the gap of expectations, so both planners and operators can have a better idea of what they could see over the lifespan of their structure.”

Source: Concordia University, www.concordia.ca.

References

1 “Concordia Researcher Hopes to Use Big Data to Make Pipelines Safer,” Concordia University Latest News, Nov. 26, 2019, https://www.concordia.ca/news/stories/2019/11/26/concordia-researcher-hopes-to-use-big-data-to-make-pipelines-safer.html?c=news/stories (Dec. 10, 2019).

2 K. Zakikhani, F. Nasiri, T. Zayed, “A Review of Failure Prediction Models for Oil and Gas Pipelines,” Journal of Pipeline Systems Engineering and Practice 11, 1 (2020).

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