A dynamic model for microbiologically influenced corrosion (MIC) integrity risk management of subsea pipelines

Microbiologically Influenced Corrosion (MIC) is a severe problem for offshore oil and gas facilities. MIC causes pinholes, which become a source of the leak. The pipeline integrity management requires preventive (proactive) (i.e., coatings, cathodic protection) and mitigative (reactive) actions (i.e., inhibitor treatment, biocide treatment). The efficiency and the cost of these integrity management actions play a critical role in overall integrity risk management. A multi-objective functional methodology involving Dynamic Continuous Bayesian Network modeling to minimize the operational risk associated with the MIC is proposed. The Meta-heuristic algorithm as Genetic Algorithm (GA) is used to obtain the optimum schedule for performing integrity management actions. The application of the proposed model is illustrated in a subsea pipeline under the influence of MIC. The results identify a series of solutions allowing decision-makers to select the optimal combination of integrity management actions with the tradeoff between reliability and cost.