Abstract
Under-deposit corrosion (UDC) and microbiologically influenced corrosion under deposits (UD-MIC) have increasingly been identified as severe forms of localized corrosion threatening the integrity of pipelines. This work utilizes a knowledge-based, semi-quantitative Bayesian approach to capture UDC and UD-MIC susceptibility and severity. This article proposed a Bayesian Network framework to study susceptibility to UDC and UDC corrosion rate. The effective corrosion rate is introduced as a measure to combine the susceptibility and corrosion rate. This measure could identify high-risk locations by assessing the probable corrosion rate while highlighting the pipeline's vulnerability to deposit settlement. Four case studies of pipeline failures due to UDC illustrate the framework's validity. A case study for a sweet gas pipeline is adapted to explore the model's robustness in assessing cases with low probabilities of UDC occurrence. The gas pipeline data, the corrosion key performance indicators spanning six years, general information on the pipeline, and the Bayesian network are made publicly available through a repository.
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