Microbiologically influenced corrosion (MIC) is one of the most important mechanisms that cause corrosion in oilfield infrastructure. Owing to the significant costs related to MIC damages, several predictive models have been proposed to measure the risk of MIC in oil systems

Principal investigator

Silvia Juliana Salgar Chaparro silvia.salgarchaparro@student.curtin.edu.au
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Area of science


Systems used


Applications used

16S rRNA analysis by Qiime, Pear, Cutadapt, Vsearch, usearch
Partner Institution: Curtin University| Project Code: microbiome-corrosion

The Challenge

The current prediction MIC tools and models available are not accurate at measuring the probability of failure, ultimately because MIC is a complex phenomenon and many of the mechanisms involved are still not completely understood. Although it is known that biofilm growth, maturation and activity are critical processes in MIC, several gaps persist in the understanding of these processes under different conditions taking place in oilfield systems, which can generate a direct impact on the biofilm corrosivity

The Solution

To overcome this, it is essential to conduct mechanistic studies using advanced microbial methodologies to isolate the microbial processes from physical and electrochemical processes, considering not only the presence of specific microbial groups but also its behaviour under different conditions. Therefore this project aims to generate relevant information about the response of the microorganisms to operational condition changes usually presented in oil production facilities such as temperature and flow rate, by applying next-generation sequencing technologies for the understanding of corrosive microbial communities.

The Outcome

Next generation sequencing provides information about the microbial species present in the corrosive environment. This analysis tell us which microbes were present, which are active, and which microbial activities were developing at the time of sampling. All of this information combined with surface techniques, chemical analysis and corrosion rates can help to understand the MIC processes. However, Next-generation sequencing data analysis requires high computational capacity. With the support of the Pawsey Supercomputing Centre we have an instance in the Nimbus cloud that allow us to analyse part of the data generated in the research.

List of Publications

Conference paper “Effect of sample storage conditions on the molecular assessment of MIC” presented at Corrosion & Prevention 2018 Conference.

Poster in the Australian Microbial Ecology Conference “Comparison of DNA and RNA-based 16S rRNA diversity profiling of the microbial community recovered from a Western Australian oil production facility”

Conference paper “Investigating the Effect of Temperature in the Community Structure of an Oilfield Microbial Consortium, and its Impact on Corrosion of Carbon Steel” in the Corrosion Conference 2019.

Journal paper under elaboration “DNA and RNA based profiling analysis for assessing the participation of the microbial community in the corrosion processes of an Australian oil production facility”

Figure 1. Microbial diversity profile of samples preserved under different conditions. Figure presented in the ACA conference 2018.


Figure 2. Electrochemical monitoring of MIC corrosion testing. Corrosion test developed to evaluate the effect of temperature in MIC. Data presented in the conference paper NACE 2019

Figure 3. Corrosion test set up for evaluating effect of different conditions in the MIC processes
Figure 4. SEM image of microbial cells y corrosion products over the surface of corroded metals