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High throughput corrosion measurement indicates that species type and concentration directly impact MIC rates. A corrosion model to identify a risk index for corrosion is based on amount, type, and proportion of living MIC organisms.
Previously we developed propidium monoazide (PMA) qPCR methods in conjunction with a beta corrosion rate model to identify and qualify the threat of microbiologically influenced corrosion (MIC) to oil and gas infrastructure. To further understand how measurements of living vs. dead microorganisms in pipeline or produced water samples would influence corrosion rates, a deeper understanding of corrosion rates induced by single or multiple species biofilms must be elucidated. We are addressing this question by re-creating and adapting a high throughput corrosion measurement device to measure the corrosion rates of multiple combinations of MIC associated groups. As this report details, species type and concentration directly impact MIC rates. This corrosion rate and bacterial concentration information will further inform our beta corrosion risk model for semi-quantitatively identifying a risk index for corrosion based on the amount, type, and proportion of living MIC organisms in specific infrastructure conditions.
Key words: Microbiologically Influenced Corrosion (MIC), biocorrosion, corrosion monitoring, molecular methods, sulfate-reducing bacteria
We describe the advancement of an activity-based quantitative polymerase chain reaction (qPCR) assay which can distinguish live from dead corrosion influencing microorganisms in oil and gas pipeline environments. We discuss the limitations and possible future optimization methods for Propidium monazide-qPCR techniques in the industry.
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Corrosion behaviors on carbon steel - in presence of sulfate reducing bacteria (SRB) - were studied via weight loss methods and electrochemical tests including polarization curve and EIS measurements.
The objective of this paper is to provide a review of various models and methods that have been developed and applied by both researchers and industry professionals to better understand and predict MIC.