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.


Microbiologically influenced corrosion (MIC) accelerates the failure of metal surfaces in the oil and gas industry, water distribution systems, transportation machinery, and of biomedical instruments. The direct cost of corrosion is estimated by NACE to be 3.1% of the U.S. GDP, which in 2013 calculated out to be $500.7 Billion. 20-40% of internal corrosion in the oil and gas industry is thought to be due to MIC.1,2,3 MIC is associated with surface pitting, resulting in more rapid corrosive failure than general corrosion.4,5,6

MIC has been implicated in several recent high profile pipeline failures, including a crude oil pipeline failure,7 a seawater pipeline failure,8 leaks in a stainless steel cooling water system of a power plant,9 and leaks in the Trans-Alaskan Pipeline.10 As new strategies requiring water for improved oil and gas extraction become more prevalent, MIC will be a reoccurring and insidious problem without the adequate understanding of MIC mechanisms and risk modelling.

A 2002 U.S. congressional study cited 25-30% of corrosion costs can be saved by adopting new pipeline inspection/monitoring methods and corrosion prevention strategies.2 The early attempts to incorporate MIC rates into a corrosion model were carried out by Pots et al.11 and adapted by Maxwell in 2006.12 In these models, the corrosion rate is calculated based on a wide range of physical and chemical parameters including pH, temperature, flow velocity, pigging frequency, use of biocide, age of the pipeline, and nutrient content, but not abundance and type or organisms present. The authors acknowledged the rates attributed to MIC are “unreliable”, however they use the “approximate” corrosion rates in these and future models none the less.11,12,13 Allison’s model in 2008 uses nutrient availability with quantities of SRB and general heterotrophic bacteria to qualitatively establish the potential for MIC in a system.14 This model calculates the potential for H2S generation with water chemistry and measurements of planktonic bacterial cells. However, there is a general consensus that a direct link seldom exists between the number of planktonic cells and the rate of MIC. In more recent models, Sorensen calculates a MIC risk factor based on the numbers of SRB and methanogenic organisms measured by qPCR, along with the reactions and stoichiometry of the electron flow at the metal surface, and from the resulting empirically determined cell-specific reaction rates.15 A model like this could be improved by considering the total amount of living and dead cells in a system with a method such as PMA-qPCR.

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