Abstract
The present study proposed the application of an integrated probabilistic model for the failure consequence assessment of oil and gas pipelines suffering under-deposit corrosion (UDC). The physics of UDC potential based on the degradation mechanism is explored and built into a network to predict the corrosion rate. The predicted corrosion rate under the prevailing corrosion mechanism is mapped into a probabilistic structure to capture their interactions on the failure state of the asset. In this study, two-level of consequences are explored for the under-deposit corrosion mechanism failure-induced prediction. The proposed approach integrates the Bayesian network with the expected utility decision theory for the cost-based consequence analysis. The dynamic and updating capacity of the integrated approach provides an intelligent monitoring advantage for asset cost-based integrity management. The model was tested on oil transmission pipelines to determine the likely consequences of failure in terms of financial losses. The result shows for the worst-case scenario, the cost values of the consequence of failure give $$1.824 \times {10}^{7}\text{ USD}$$ , $$1.245 \times {10}^{5}\text{ USD}$$ , and $$1.082 \times {10}^{8}\text{ USD}$$ for the loss of production, compensation, and environmental impact, respectively. The present approach offers a cost-based assessment tool that could guide a well-informed integrity management strategy for oil and gas operations in harsh environments.
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