One of the leading threats to the integrity of oil and gas transmission pipeline systems is metal-loss corrosion.  This threat is commonly managed by evaluating measurements obtained with in-line inspection tools, which locate and size individual metal-loss defects.  Both deterministic and probabilistic methods are used in the pipeline industry to evaluate the severity of these defects. Probabilistic evaluations typically utilize structural reliability, which is an approach to designing and assessing structures that focuses on the calculation of the probability that a structure may fail. Reliability methods have been applied to pipeline corrosion defects since the 1990’s, and have continually remained an active area of research and development.

The basic equations of the structural reliability approach involve a multi-dimensional integral which must be solved in order to obtain the probability of failure.  This solution can be obtained using either analytical or numerical methods.  While complex analytical methods such as the First-Order Reliability Method (FORM) have been successfully applied to pipeline corrosion, the most common and straight-forward approach is to use Monte Carlo simulation. While Monte Carlo simulation is robust, it is also computationally intensive and therefore can be time-consuming to obtain stable estimates at small probability levels (ex. less than 10^-6^)~.~ To deal with this issue, many variance-reduction approaches have been utilized such as importance sampling, however this approach is not always robust for time-dependent reliability problems with multiple limit states.  

An alternative variance reduction approach, called Subset Simulation, views the rare failure probability as the product of a series of larger, conditional failure probabilities by introducing intermediate events. These conditional failure probabilities cannot be estimated using simple Monte Carlo, so a Markov Chain Monte Carlo methodology called the Modified Metropolis algorithm is utilized instead. Subset Simulation provides an efficient solution to reliability problems without the need for prior information about the system’s behavior, other than an input-output model.  Subset simulation has proven so effective, that it has been applied to rare-event reliability problems in disciplines such as geotechnical, aerospace, fire and nuclear engineering.

This paper presents comparisons between the probability of failure estimates generated by Monte Carlo simulation and Subset Simulation, for various pipeline corrosion defect cases. These cases illustrate the increased accuracy and decreased computational effort required for Subset Simulation. By significantly reducing the computational effort required to obtain stable estimates of small failure probabilities, this methodology reduces one of the major barriers to the use of reliability methods for system-wide pipeline reliability assessment.


Subset Simulation for Structural Reliability Analysis of Pipeline Corrosion Defects

Division / Sponsors: 
Pipeline Systems Division

Paper Proceedings: https://asmedigitalcollection.asme.org/IPC/IPC2020/volume/84447

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