Meta-analysis of microbial communities in worldwide petroleum fields: An international analysis between 16S and shotgun metagenomic datasets.

Abstract

Microorganisms inhabiting petroleum reservoirs have diverse contribution and impact to the upstream facilities in oil fields. The identity of these microbes and their functionality are diverse, complex and still poorly understood. This thesis aimed to investigate the microbial community of petroleum reservoir and map out its functionality and influence on oil fields facilities. Metagenome datasets from worldwide oil reservoirs were collected in public databases and assessed through implementing a robust bioinformatic pipeline to decipher taxonomic profiles of microbial communities. The analysis was performed on nine public datasets, 4 shotgun and 5 16S metagenomic datasets with a total of 109 samples. The bioinformatic pipeline was divided into two parts targeting the taxonomy and potential function of metagenomic datasets. The bacterial community was dominated by members of phyla Proteobacteria and Firmicutes, while class Methanobacteria was majorly composing the archaeal community. The potential core biome analysis showed that phyla Proteobacteria and Firmicutes are the core members at 80% frequency and 10% abundance cutoffs. No core was found for archaeal community. The influence of environmental variables was detected as Na and Cl ions were found to be the strongest influences on the bacterial and archaeal communities. However, there was minor observed influence from environmental factors such as Temperature, due to limitations in the number of data points and confounders arising from study methodologies and annotation. Thus, there is strong need for consistent data collection and uploading practices to maximize the valorization of such data Based on functional analysis, sulfite reductase gene were found as common feature in the shotgun datasets, detecting a risk of developing souring problems. These outcomes expand the knowledge of microbial structure in these ecosystems.

Date of Award	Dec 2021
Original language	American English