Microbiologically influenced corrosion (MIC) creates multiple defects. The interaction of MIC defects and their time dependence need to be considered for robust safety assessment of the asset.

This paper presents a methodology for the dynamic safety assessment of the assets under the influence of MIC. The methodology is built by integrating the Bayesian Network (BN)-Markov Mixture (MM) technique with Monte Carlo simulation. The integration of BN and MM provides an empirical model to probabilistically predict the effective defect growth rate based on the multiple defects’ interaction. A rate-dependent stochastic formulation is also developed for the remaining strength and safe operating pressure prediction using the Monte Carlo simulation. The proposed methodology dynamically predicts and captures the evolving effect of corrosion defects’ interaction and effective defect growth rate on the remaining strength and survival likelihood of an in-service corroding asset. The methodology is tested on an offshore pipeline, and the dynamic effects of corrosion influencing parameters and defects’ interaction on the pipeline survivability were predicted. Critical safety influencing factors of the pipeline under complex microbial biofilm architecture were identified. The proposed methodology provides a parametric-based condition monitoring tool for effective management of MIC and ensuring safety in offshore systems.

受微生物影响的腐蚀（MIC）会产生多个缺陷。为了对资产进行可靠的安全评估，需要考虑MIC缺陷的相互作用及其时间依赖性。

本文提出了一种在中等收入国家影响下对资产进行动态安全评估的方法。该方法是通过将贝叶斯网络（BN）-马尔可夫混合物（MM）技术与蒙特卡洛模拟相结合而构建的。BN和MM的集成提供了一个经验模型，可以基于多个缺陷的相互作用来概率地预测有效缺陷的增长率。还开发了速率相关的随机公式，以使用蒙特卡洛模拟来预测剩余强度和安全工作压力。所提出的方法可以动态预测并捕获腐蚀缺陷相互作用和有效缺陷增长率对在役腐蚀资产的剩余强度和生存可能性的影响。该方法已在海上管道上进行了测试，预测了腐蚀影响参数和缺陷相互作用对管道生存能力的动态影响。确定了复杂微生物生物膜结构下管道的关键安全影响因素。所提出的方法提供了一种基于参数的状态监测工具，可有效管理MIC并确保海上系统的安全。