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51318-11398- Modeling of Microbiologically Influenced Corrosion (MIC) in the Oil and Gas Industry - Past, Present and Future

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  • 51318-11398- Modeling of Microbiologically Influenced Corrosion (MIC) in the Oil and Gas Industry - Past, Present and Future
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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.

Product Number: 51318-11398-SG
Author: John Wolodko / Tesfaalem Haile / Faisal Khan / Christopher Taylor / Richard Eckert / Seyed Javad Hashemi / Andrea Marciales Ramirez / Torben Lund Skovhus
Publication Date: 2018
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Microbiologically Influenced Corrosion (MIC) is a complex form of materials degradation caused by the biological activity of microorganisms such as bacteria, archaea and fungi. It is typically characterized by the presence of microbiological populations within a biofilm or semi-solid deposit resulting in localized and accelerated corrosion. While MIC has been actively studied for decades, there is still a significant gap in the ability to accurately predict MIC rates. This is due, in part, to a limited understanding of all the microbiological communities involved in MIC, and the complexity of biological, chemical and operational parameters responsible for MIC. For the oil and gas sector, the threat of MIC can be particularly challenging since it can affect a wide range of operations including upstream production and processing facilities (onshore and offshore), mid-stream and transmission pipelines and water systems. Compared to other corrosion threats, the detection of MIC is typically reactive rather than pro-active (i.e., MIC is difficult to predict and is most often detected after an inspection or failure). As such, there is

a continued demand for validated predictive tools to assist in managing the threat of MIC. 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. This includes a number of phenomenological and mechanistic models that have been developed by the research community to help explain specific MIC mechanisms or predict corrosion rates, and a number of risk-based models applied by industry to screen and rank the potential of MIC threats. The advantages and disadvantages of each modeling approach are summarized, along with a discussion of new potential methods such as molecular modeling, risk based inspection (RBI) and Integrated Computational Materials Engineering (ICME).

Key words: Microbiologically Influenced Corrosion, MIC, Modeling, RBI, ICME

 

Microbiologically Influenced Corrosion (MIC) is a complex form of materials degradation caused by the biological activity of microorganisms such as bacteria, archaea and fungi. It is typically characterized by the presence of microbiological populations within a biofilm or semi-solid deposit resulting in localized and accelerated corrosion. While MIC has been actively studied for decades, there is still a significant gap in the ability to accurately predict MIC rates. This is due, in part, to a limited understanding of all the microbiological communities involved in MIC, and the complexity of biological, chemical and operational parameters responsible for MIC. For the oil and gas sector, the threat of MIC can be particularly challenging since it can affect a wide range of operations including upstream production and processing facilities (onshore and offshore), mid-stream and transmission pipelines and water systems. Compared to other corrosion threats, the detection of MIC is typically reactive rather than pro-active (i.e., MIC is difficult to predict and is most often detected after an inspection or failure). As such, there is

a continued demand for validated predictive tools to assist in managing the threat of MIC. 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. This includes a number of phenomenological and mechanistic models that have been developed by the research community to help explain specific MIC mechanisms or predict corrosion rates, and a number of risk-based models applied by industry to screen and rank the potential of MIC threats. The advantages and disadvantages of each modeling approach are summarized, along with a discussion of new potential methods such as molecular modeling, risk based inspection (RBI) and Integrated Computational Materials Engineering (ICME).

Key words: Microbiologically Influenced Corrosion, MIC, Modeling, RBI, ICME

 

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