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Reactive Transport Model of Sulfur Cycling as Impacted by Perchlorate and Nitrate Treatments
Author(s) -
Yiwei Cheng,
Christopher G. Hubbard,
Li Li,
Nicholas Bouskill,
Sergi Molins,
Liange Zheng,
Eric Sonnenthal,
Mark E. Conrad,
Anna Engelbrektson,
John D. Coates,
Jonathan AjoFranklin
Publication year - 2016
Publication title -
environmental science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.851
H-Index - 397
eISSN - 1520-5851
pISSN - 0013-936X
DOI - 10.1021/acs.est.6b00081
Subject(s) - nitrate , perchlorate , sulfate , permeable reactive barrier , environmental remediation , environmental chemistry , sulfur , chemistry , nitrite , heterotroph , hydrogen sulfide , environmental science , environmental engineering , ecology , biology , organic chemistry , bacteria , genetics , contamination , ion
Microbial souring in oil reservoirs produces toxic, corrosive hydrogen sulfide through microbial sulfate reduction, often accompanying (sea)water flooding during secondary oil recovery. With data from column experiments as constraints, we developed the first reactive-transport model of a new candidate inhibitor, perchlorate, and compared it with the commonly used inhibitor, nitrate. Our model provided a good fit to the data, which suggest that perchlorate is more effective than nitrate on a per mole of inhibitor basis. Critically, we used our model to gain insight into the underlying competing mechanisms controlling the action of each inhibitor. This analysis suggested that competition by heterotrophic perchlorate reducers and direct inhibition by nitrite produced from heterotrophic nitrate reduction were the most important mechanisms for the perchlorate and nitrate treatments, respectively, in the modeled column experiments. This work demonstrates modeling to be a powerful tool for increasing and testing our understanding of reservoir-souring generation, prevention, and remediation processes, allowing us to incorporate insights derived from laboratory experiments into a framework that can potentially be used to assess risk and design optimal treatment schemes.

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