Next generation modeling of microbial souring – Parameterization through genomic information

• Published in: International biodeterioration & biodegradation 2018-01, Vol.126 (C), p.189-203
• Main Authors: Cheng, Yiwei, Hubbard, Christopher G., Zheng, Liange, Arora, Bhavna, Li, Li, Karaoz, Ulas, Ajo-Franklin, Jonathan, Bouskill, Nicholas J.
• Format: Article
• Language: English
• Subjects: Amino acids; Biosynthesis; Communities; Community composition; Computer simulation; ENVIRONMENTAL SCIENCES; Genomics; GEOSCIENCES; Hydrogen sulfide; Impact analysis; Information processing; Kinetics; Mathematical models; Microbially mediated sulfate reduction; microbially meditated sulfate reduction; Microorganisms; Mitigation; Modelling; Multiphase reactive transport model; Oil and gas exploration; Oil and gas production; Oil reservoir; Oil reservoirs; Parameterization; Petroleum production; Reaction kinetics; Seawater; Studies; Sulfate reduction; Sulfates; Sulfide
• ISSN: 0964-8305; 1879-0208
• DOI: 10.1016/j.ibiod.2017.06.014

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. •Utilize genomic data to constrain the biological parameters in reactive transport models.•Minimum generation times calculated based on codon usage bias.•Optimal growth temperatures calculated based frequency of amino acids.•Model sulfate reducing microbial community composition as an emergent property.•Simulations demonstrate trade off between community complexity and simulation run time.