Next generation modeling of microbial souring – Parameterization through genomic information. (January 2018)

• Record Type: Journal Article
• Authors: Cheng, Yiwei; Hubbard, Christopher G.; Zheng, Liange; Arora, Bhavna; Li, Li; Karaoz, Ulas; Ajo-Franklin, Jonathan; Bouskill, Nicholas J.
• Is Part Of: International biodeterioration & biodegradation. Volume 126(2018)
• Page Start: 189
• Page End: 203
• Publication Date: 2018-01
• Subjects: Microbially mediated sulfate reduction -- Oil reservoir -- Genomics -- Multiphase reactive transport model; Biodegradation -- Periodicals; Bioremediation -- Periodicals; Biodegradation -- Periodicals; Biodégradation -- Périodiques; Biorestauration -- Périodiques; Electronic journals; 620.11223
• DOI: 10.1016/j.ibiod.2017.06.014
• Languages: English
• ISSNs: 0964-8305

Abstract

Abstract: Biogenesis of hydrogen sulfide (H2 S) (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. Highlights: 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.