The corrosion of ferrous metals in the crude oil industry costs hundreds of millions of dollars annually. It was estimated that about 15% of metal corrosion is due to bacterial activities in a process known as microbiologically-influenced corrosion (MIC). The aim of this study is to develop the method to identify microbial organisms that play key roles for MIC by an analysis of metagenome with bioinformatics.
We obtained scraper samples that were generated in pipe wash operations from seawater injection pipelines and oil pipelines of the oil plants in KSA. We extracted DNA from the samples and generated the 16S rRNA amplicon metagenomes. We also retrieved publically available metagenome data of MIC sites (mainly from USA) from NCBI, using them for the comparison with our metagenomes.
The metagenomes of our scraper samples clearly showed that microflora in seawater were very similar among samples and among oil pipeline samples, respectively. On the other hand, microflora were quite different between seawater and oil pipeline samples. The clustering analysis of our data together with public metagenomes also showed that metagenomes of seawater and oil pipelines were clustered separately. In addition, we found that metagenomes of oil pipeline formed a cluster with metagenomes of MIC sites at deep subsurface environments, showing that microflora in oil pipeline shared the common characteristics of the microbial composition among MICs in anaerobic environments. These results suggest that we should take different strategies against MICs at least between seawater and oil pipelines.
Our results suggest that key microorganisms of MIC are distinct between seawater pipeline and oil pipeline. Because we are producing the Big Data of the MIC metagenomes, we are planning to utilize AI for functional annotation from taxonomic information. Our study leads to the development of a new method to predict, diagnose and provide appropriate treatment for MIC.
Building 9 - Lecture hall 2
13:45 - 14:00