Abstract
Microbiologically influenced corrosion (MIC) research in the oil and gas industry has seen a revolution over the past decade with the increased application of molecular microbiological methods (MMM) and new industry standards. Models can provide numerous benefits, e.g., guidance on MIC mitigation selection and prioritization, identification of data gaps, a scientific basis for risk-based inspections, and technical justification for asset design and life-extension. This chapter describes trends in MIC modeling: different types of models, future needs, and the utility of MIC models from an end-user perspective. The models use qualitative, semiquantitative, or quantitative measures to help predict the rate of degradation caused by MIC and other threats. A new model that links MIC in topsides oil processing systems with risk-based inspection (RBI) through the application of data obtained by MMMs, and its implementation, is presented and discussed. Integrated computational materials engineering (ICME) is a promising future approach for prediction and management of MIC, using translational research to deliver new modeling tools to industry in the shortest possible development time.
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