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A model flow cell system was designed to investigate pitting corrosion in pipelines associated with microbial communities. A microbial inoculum producing copious amounts of H2S was enriched from an oil pipeline biofilm sample. Reservoirs containing a nutrient solution and the microbial inoculum were pumped continuously through six flow cells containing mild steel corrosion coupons. Two cells received corrosion inhibitor “A”, two received corrosion inhibitor “B”, and two (“untreated”) received no additional chemicals. Coupons were removed after 1 month and analyzed for corrosion profiles and biofilm microbial communities. Coupons from replicate cells showed a high degree of similarity in pitting parameters and in microbial community profiles, as determined by 16S rRNA gene sequence libraries but differed with treatment regimen, suggesting that the corrosion inhibitors differentially affected microbial species. Viable microbial biomass values were more than 10-fold higher for coupons from flow cells treated with corrosion inhibitors than for coupons from untreated flow cells. The total number of pits >10 mils diameter and maximum pitting rate were significantly correlated with each other and the total number of pits with the estimated abundance of sequences classified as Desulfomicrobium. The maximum pitting rate was significantly correlated with the sum of the estimated abundance of Desulfomicrobium plus Clostridiales, and with the sum of the estimated abundance of Desulfomicrobium plus Betaproteobacteria. The lack of significant correlation with the estimated abundance of Deltaproteobacteria suggests not all Deltaproteobacteria species contribute equally to microbiologically influenced corrosion (MIC) and that it is not sufficient to target one bacterial group when monitoring for MIC.

Investigating the susceptibility of various fuels to anaerobic biodegradation has become complicated with the recognition that the fuels themselves are not sterile. Bacterial DNA could be obtained when various fuels were filtered through a hydrophobic teflon (0.22 μm) membrane filter. Bacterial 16S rRNA genes from these preparations were PCR amplified, cloned, and the resulting libraries sequenced to identify the fuel-borne bacterial communities. The most common sequence, found in algal- and camelina-based biofuels as well as in ultra-low sulfur diesel (ULSD) and F76 diesel, was similar to that of a Tumebacillus. The next most common sequence was similar to Methylobacterium and was found in the biofuels and ULSD. Higher level phylogenetic groups included representatives of the Firmicutes (Bacillus, Lactobacillus and Streptococcus), several Actinobacteria, Deinococcus-Thermus, Chloroflexi, Cyanobacteria, Bacteroidetes, Alphaproteobacteria (Methylobacterium and Sphingomonadales), Betaproteobacteria (Oxalobacteraceae and Burkholderiales) and Deltaproteobacteria. All of the fuel-associated bacterial sequences, except those obtained from a few facultative microorganisms, were from aerobes and only remotely affiliated with sequences that resulted from anaerobic successional events evident when ULSD was incubated with a coastal seawater and sediment inoculum. Thus, both traditional and alternate fuel formulations harbor a characteristic microflora, but these microorganisms contributed little to the successional patterns that ultimately resulted in fuel decomposition, sulfide formation and metal biocorrosion. The findings illustrate the value of molecular approaches to track the fate of bacteria that might come in contact with fuels and potentially contribute to corrosion problems throughout the energy value chain.