REPETITIVE SAMPLING AND CONTROL THRESHOLD IMPROVE 16S RRNA GENE SEQUENCING RESULTS FROM NIOBRARA SHALE PRODUCED WATER

Many deep hydrocarbon reservoirs contain microbial ecosystems that are poorly understood. Unfortunately, waters associated with these subsurface environments generally have low biomass concentrations that are subject to contamination by foreign DNA, which can produce difficulties in interpreting 16S rRNA gene sequencing results. Additionally, sample collection from hydrocarbon wells presents logistical difficulties, as researchers are often restricted in terms of access and time permitted on site. Sampling plans should therefore be designed in advance to produce optimal results. This study aims to assist in standardizing microbial biomass sampling through the development of a sampling strategy for 16S rRNA gene sequencing from low biomass oil- and gas-producing environments.

For this study, forty-nine different samples were collected by filtering specific volumes of produced water from one mature hydraulically fractured well producing from the Niobrara Shale. Ten specific volumes of water were filtered four to five different times to create a robust sample set. Eight negative control blanks were also collected. DNA was extracted from each sample, and quantitative polymerase chain reaction (qPCR) and 16S rRNA Illumina MiSeq gene sequencing were performed to determine relative concentrations of biomass and microbial community composition, respectively. The qPCR results varied across sampled volumes, while no correlation existed between crossing point value and sample volume. We found that a larger sample volume may not result in greater biomass concentrations or better representation of a sampled environment. This may be due to high variability in the concentration and types of microbial communities present in produced waters over short (i.e., hours) time scales. These results suggest that researchers should prioritize collection of many low-volume samples, some of which might have sufficient biomass for successful 16S rRNA gene sequencing, over few high-volume samples. Submission of multiple control blanks is also vital to determine how contamination or low biomass may influence a sample set collected from a hydrocarbon environment.