Preserving Microbial Community Integrity in Oilfield Produced Water.

Determining a representative microbial signature from any given location is dependent on robust sample collection and handling. Different sampling locations and hence sample properties can vary widely; for example, soil would be collected and handled differently compared to liquid samples. In the event that sample material has a low concentration of biomass, large quantities need to be collected for microbial community analysis. This is certainly the case when investigating the microbiology of oilfield systems, wherein produced water (PW) is one of the most common sources for microbial sampling. As the detrimental effects of microbial metabolism within these industrial milieus are becoming increasingly well-established, the characterization of microbial community composition using molecular biological analyses is becoming more commonplace for accurate monitoring. As this field continues to develop, the importance for standardized operating protocols cannot be understated, so that industry can make the most informed operational decisions possible. Accurately identifying oilfield microbial communities is paramount, as improper preservation and storage following sample collection is known to lead to erroneous microbial identifications. Preserving oilfield PW can be challenging, as many locations are remote, requiring lengthy periods of time before samples can be processed and analyzed. While previous studies have characterized the effects of various preservatives on concentrated, filtered, or purified microbial samples, to the best of our knowledge, no such study has been undertaken on low biomass liquid samples. To this end, we investigated the effectiveness of nine different preservation conditions on PW collected from the same sampling location within a heavy-oil producing field, and monitored how the microbial community changed over the period of a month. Our results reveal that the choice of preservative drastically affects microbial community, and should be selected with careful consideration before sampling occurs.