Bayesian Network statistical methodology turns uncertainty into an ally of risk managers
Operators need not wait for all data to perform risk assessments
DNV GL’s MARV™ approach applies the method to pipelines worldwide
US pipeline operator SoCalGas sees operational and potential compliance benefits in using MARV
Gas pipeline operators are discovering how a method called Bayesian Networks can, combined with machine learning, address data uncertainties in risk management (Figure 1).1 This approach converts uncertainty into an ally rather than an enemy of pipeline risk managers.
Choosing which data to gather first is complicated. Data may be missing or unreliable. There may also be uncertainty over failure mechanisms such as corrosion, third-party damage and others. In addition, collecting and assessing the validity of data from sensors on pipelines, inspections and front-line operatives is expensive. With safety at the top of the agenda, operators therefore have tough cost-benefit decisions to make when designing data-driven risk management strategies.
When data is missing, traditional risk assessment methodologies require operators to conduct pipeline risk assessments based on worst-case scenarios. “With smart risk assessments based on Bayesian Networks, they do not need to gather all the data in the same way,” said Dr Francois Ayello , principal engineer, risk management, DNV GL - Oil & Gas.
“Bayesian Networks is the logical next step in pipeline risk management because it incorporates data uncertainty and provides justification for how much an operator should invest in collecting data about buried assets,” said Gordon Ye , supervising gas engineer of data, risk & threat, for the Gas Transmission Integrity Management (TIMP) side of US utility Pacific Gas and Electric (PG&E).
“This saves cost and time, while keeping threats below acceptable levels," added Ayello. “Our work applying the methodology for pipeline operators shows they need not wait for all the data to do all risk assessment, and this obviously saves resources that can be spent on another pipeline.”2