Abstract
This work presents a new probabilistic methodology and model to estimate the microbiologically influenced corrosion (MIC) rate. The proposed methodology considers the variability of the corrosion causing parameters, complex interdependencies of the parameters, and updating the corrosion rate in response to evolving conditions. The proposed method is used to develop a fully parameterized Bayesian network model for the MIC rate. The model is tested using MIC field data. The results show that the metabolism of iron-oxidizing bacteria and methanogens are the most probable contributors to the corrosion rate. The study also identifies the most sensitive parameters affecting the corrosion rate. The proposed model plays a vital role in safety assessment and corrosion risk management of oil and gas production and processing assets.
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