This paper studies the application of Dynamic Bayesian Networks (DBNs) for modeling degradation processes in oil and gas pipelines. A DBN tool consisting of a matlab code has been developed for performing inference on models. The tool is then applied for probabilistic modeling of the burst pressure of a pipe subjected to corrosion degradation and for safety assessment. The burst pressure is evaluated using the ASME B31G design method and other empirical formulas. A model for corrosion prediction in pipelines and its governing parameters are explicitly included into the probabilistic framework. Different sets of simulated corrosion measurements are used to increase the accuracy of the model predictions. Several parametric studies are conducted to investigate how changes in the observed corrosion (depth and length) and in the frequency of inspections affect the pipe reliability.
Issue Section:
Structures and Safety Reliability
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