Modeling of Microbiologically Influenced Corrosion (MIC) for Risk-Based Inspection (RBI) in the Oil and Gas Industry

Abstract
Microbiologically Influenced Corrosion (MIC) causes considerable losses to the oil and gas industry annually. Although studies on MIC have become more frequent over the last few years, it is still not fully understood. This poses a significant challenge to operators of oil and gas facilities since methodologies and guidelines for MIC assessment and mitigation are still evolving. The objective of this study is to develop a practical risk-based inspection (RBI) methodology for industry which specifically incorporates MIC threats. The RBI approach is based on semi-quantitative methods which aim to 1) identify localized "hot spots" for possible MIC threats (screening), and 2) help establish maintenance priorities and frequency (rank both facilities and pipelines based on the risk). This new RBI approach could be used for both onshore and offshore facilities, and will incorporate modern techniques to quantify microbiological abundance, diversity, activity and function. Furthermore, the assessment method will be tailored for certain archetypes of the oil and gas industry (e.g. crude gathering systems, seawater injection systems and produced water systems), and will account for various levels of data availability. Risk of potential MIC threats is quantified by determining the probability of failure (PoF) and the associated consequence of failure (CoF). For evaluating the PoF, the model systematically considers metallurgical, chemical, biological and physical/operating parameters based on information from operations (e.g. inspection data) and the literature. To implement the model, a decision tree (flow-chart) has been developed which will provide end users with an easy tool to assess and screen assets for potential MIC threats.