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Abstract

Qatar's economic emergence is influenced in great part by its gas reservoirs. Hence, gas processing is considered as one of the major steps to aid Qatar in its transition towards a more diversified, prospered and innovative economy. However, there is a need for research to assess the quality of water in the cooling system used in the manufacture of petroleum products. Thus, assessing water quality can help complement the technologies used for gas processing and address the challenges facing gas industries. One of these problems is pipeline anaerobic iron corrosion induced by the activity of environmental microorganism. Such microbially influenced corrosion (MIC) has various technical and economic implications that have major effects on gas and oil processing. It has been suggested that the presence of sulfur-reducing bacteria (SRB) in the cooling water in oil and gas pipeline is considered as the main culprit of such corrosions due to the pipeline anoxic conditions. Thus, examining water security, which is the aversion of water-related risks, in oil and gas industry is needed. Such risks may include disrupted operations and economic losses. Thus, there is a need to improve water use efficiency in the oil industry to improve oil and gas production and the process of shipping it to different destinations. Hence, our research aims to obtain water samples from different points of the oil-gas pipelines in Qatar and carry out microbial classification. More specifically, the aim of our research is to obtain reverse transcription polymerase chain reaction (RT-PCR) sequenced DNA data using the Ion Torrent method and analyze them. First, multiple samples containing various bacterial species were obtained from different points of the oil-gas pipelines in Qatar. Then RT-PCR was carried out on these samples using specific primers that can hybridize to the conserved regions of the 16S ribosomal RNA region. The PCR amplicons were then sequenced using the Ion Torrent device. The next step is to reconstruct the 16S ribosomal RNA (rRNA) region using the computational genomics tool, SPAdes, and aim for a good coverage and a good alignment score. SPAdes is a genomic assembler that uses iterative mapping of short reads and blast them against reference sequences to reconstruct 16S rRNA genes. The reconstructed regions would be from different bacterial species and each bacteria would have its own hypervariable region that serves as a fingerprint. Hence, the reconstructed 16S ribosomal RNA (rRNA) sequences are considered reliable markers for the taxonomic classification and phylogenetic analysis of bacteria. Eventually, the data is classified and filtered using Ribosomal Database Project (RDP) classifier 2.3, which is then followed by manual extraction, categorization and evaluation. RDP is a naive Bayesian classifier that can rapidly classify bacterial 16S rRNA sequences into the new higher-order taxonomy proposed in Bergey's Taxonomic Outline of the Prokaryotes. This online classifier provides taxonomic assignments from domain to genus.Our results show that the water samples contain high percentage of the sulfur-reducing bacteria (SRB) when compared to the percentage of other microbes in the water samples. This emphasizes on the necessity to improve water security of the oil-gas cooling system by reducing the percentage of such bacteria. Hence, one of the potential future direction of this project is to identify bacteriophages that have the ability to eliminate the sulfur reducing bacteria from the cooling water system and thus reduce the chances of water contamination.

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/content/papers/10.5339/qfarc.2018.EEPD1089
2018-03-12
2019-11-20
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http://instance.metastore.ingenta.com/content/papers/10.5339/qfarc.2018.EEPD1089
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