An Intelligent Cost-Based Consequence Model for Offshore Systems in Harsh Environments

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.