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

Abstract

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 themore » original SRM.« less

Authors:
 [1];  [2];  [3];  [3];  [4];  [1];  [3];  [1]
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  1. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Climate and Ecosystem Sciences Division
  2. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Energy Geosciences Division; Cardiff Univ., Cardiff (Wales). Walter Research Inst.
  3. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Energy Geosciences Division
  4. Pennsylvania State Univ., University Park, PA (United States). Dept. of Civil and Environmental Engineering
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1567110
Grant/Contract Number:  
AC02-05CH11231
Resource Type:
Accepted Manuscript
Journal Name:
International Biodeterioration and Biodegradation
Additional Journal Information:
Journal Volume: 126; Journal Issue: C; Journal ID: ISSN 0964-8305
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
58 GEOSCIENCES; 54 ENVIRONMENTAL SCIENCES; microbially meditated sulfate reduction; oil reservoir; genomics; multiphase reactive transport model

Citation Formats

Cheng, Yiwei, Hubbard, Christopher G., Zheng, Liange, Arora, Bhavna, Li, Li, Karaoz, Ulas, Ajo-Franklin, Jonathan, and Bouskill, Nicholas J. Next generation modeling of microbial souring – Parameterization through genomic information. United States: N. p., 2018. Web. doi:10.1016/j.ibiod.2017.06.014.
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Cheng, Yiwei, Hubbard, Christopher G., Zheng, Liange, Arora, Bhavna, Li, Li, Karaoz, Ulas, Ajo-Franklin, Jonathan, & Bouskill, Nicholas J. Next generation modeling of microbial souring – Parameterization through genomic information. United States. https://doi.org/10.1016/j.ibiod.2017.06.014
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Cheng, Yiwei, Hubbard, Christopher G., Zheng, Liange, Arora, Bhavna, Li, Li, Karaoz, Ulas, Ajo-Franklin, Jonathan, and Bouskill, Nicholas J. Mon . "Next generation modeling of microbial souring – Parameterization through genomic information". United States. https://doi.org/10.1016/j.ibiod.2017.06.014. https://www.osti.gov/servlets/purl/1567110.
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@article{osti_1567110,
title = {Next generation modeling of microbial souring – Parameterization through genomic information},
author = {Cheng, Yiwei and Hubbard, Christopher G. and Zheng, Liange and Arora, Bhavna and Li, Li and Karaoz, Ulas and Ajo-Franklin, Jonathan and Bouskill, Nicholas J.},
abstractNote = {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.},
doi = {10.1016/j.ibiod.2017.06.014},
journal = {International Biodeterioration and Biodegradation},
number = C,
volume = 126,
place = {United States},
year = {Mon Jan 01 00:00:00 EST 2018},
month = {Mon Jan 01 00:00:00 EST 2018}
}
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Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
https://doi.org/10.1016/j.ibiod.2017.06.014
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Cited by: 13 works
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Figures / Tables:

Table 1 Table 1: Information on the 25 SRM from which we derived average minimum generation time and optimal growth temperature.
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