Direct assessment (DA) is a valuable tool for risk management used across the pipeline industry. To carry out DA, pipeline operators usually follow NACE(1) standards such as NACE SP0502, SP0206 or SP 0208. Each of these standards requires utilizing an appropriate model to assess and predict pipeline corrosion rate. One type of model that attracts attention in recent years is probabilistic models especially those created based on Bayesian Networks. Bayesian Networks is particularly well suited to help the DA process because the methodology allows combining mechanistic models with expert's knowledge, additionally, it allows these models to use any type of information to update risk results.

In this paper, the applications of Bayesian Networks(BN) based probabilistic model were detailed in the indirect assessment step of the DA process and case studies were introduced where probabilistic corrosion models based on Bayesian Networks were developed for assessing both pipeline internal and external corrosions.

Subject
Mechanistic models, External corrosion direct assessment, Water, Modeling, Corrosion rate, Pipelines, Probability, Coating damage, Mechanical failure, Corrosion modeling, Corrosion data, Internal corrosion direct assessment, External corrosion
Keywords:
Pipeline, Direct assessment, Bayesian networks, Probabilistic modeling, Corrosion
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2019
Association for Materials Protection and Performance (AMPP)
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