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51318-10889- Development and Testing of a Laboratory Based MIC Corrosion Rate Measurement Device

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.

Product Number: 51318-10889-SG
Author: Sarah Eisenlord / Mike Mensinger Jr. / Scott Leleika / Ryan Feist
Publication Date: 2018
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$20.00

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

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

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