Next generation modeling of microbial souring – Parameterization through genomic information

Cheng, Yiwei; Hubbard, Christopher G.; Zheng, Liange; Arora, Bhavna; Li, Li; Karaoz, Ulas; Ajo-Franklin, Jonathan; Bouskill, Nicholas J.

doi:10.1016/j.ibiod.2017.06.014

Title: Next generation modeling of microbial souring – Parameterization through genomic information

Journal Article · Mon Jan 01 00:00:00 EST 2018 · International Biodeterioration and Biodegradation

DOI:https://doi.org/10.1016/j.ibiod.2017.06.014· OSTI ID:1567110

Cheng, Yiwei ^[1]; Hubbard, Christopher G. ^[2]; Zheng, Liange ^[3]; Arora, Bhavna ^[3]; Li, Li ^[4]; Karaoz, Ulas ^[1]; Ajo-Franklin, Jonathan ^[3]; Bouskill, Nicholas J. ^[1]

Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Climate and Ecosystem Sciences Division
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Energy Geosciences Division; Cardiff Univ., Cardiff (Wales). Walter Research Inst.
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Energy Geosciences Division
Pennsylvania State Univ., University Park, PA (United States). Dept. of Civil and Environmental Engineering

Biogenesis of hydrogen sulfide (H₂S) (microbial souring) has detrimental impacts on oil production operations and can cause health and safety problems. Understanding the processes that control the rates and patterns of sulfate reduction is crucial in developing a predictive understanding of reservoir souring and associated mitigation processes. This work demonstrates an approach to utilize genomic information to constrain the biological parameters needed for modeling souring, providing a pathway for using microbial data derived from oil reservoir studies. Minimum generation times were calculated based on codon usage bias and optimal growth temperatures based on the frequency of amino acids. We show how these derived parameters can be used in a simplified multiphase reactive transport model by simulating the injection of cold (30 °C) seawater into a 70 °C reservoir, modeling the shift in sulfate reducing microorganisms (SRM) community composition, sulfate and sulfide concentrations through time and space. Finally, we explore the question of necessary model complexity by comparing results using different numbers of SRM. Simulations showed that the kinetics of a SRM community consisting of twenty-five SRM could be adequately represented by a reduced community consisting of nine SRM with parameter values derived from the mean and standard deviations of the original SRM.

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Research Organization:: Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC)

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 1567110

Journal Information:: International Biodeterioration and Biodegradation, Vol. 126, Issue C; ISSN 0964-8305

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 13 works

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