Lateral migration patterns toward or away from injection wells for earthquake clusters in Oklahoma
Type
PosterKAUST Department
Computational Earthquake Seismology (CES) Research GroupEarth Science and Engineering Program
Physical Science and Engineering (PSE) Division
Date
2020-03-09Permanent link to this record
http://hdl.handle.net/10754/660713
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Exploring the connections between injection wells and seismic migration patterns is key to understanding processes controlling growth of fluid-injection induced seismicity. Numerous seismic clusters in Oklahoma have been associated with wastewater disposal operations, providing a unique opportunity to investigate migration directions of each cluster with respect to the injection-well locations. We introduce new directivity migration parameters to identify and quantify lateral migration toward or away from the injection wells. We take into account cumulative volume and injection rate from multiple injection wells. Our results suggest a weak relationship between migration direction and the cluster-well distances. Migration away from injection wells is found for distances shorter than 5-13 km, while an opposite migration towards the wells is observed for larger distances, suggesting an increasing influence of poroelastic stress changes. This finding is more stable when considering cumulative injected volume instead of injection rate. We do not observe any relationship between migration direction and injected volume or equivalent magnitudes.Citation
López-Comino, J. Á., Galis, M., Mai, P. M., Chen, X., and Stich, D.: Lateral migration patterns toward or away from injection wells for earthquake clusters in Oklahoma , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2649, https://doi.org/10.5194/egusphere-egu2020-2649, 2020Publisher
Copernicus GmbHAdditional Links
https://meetingorganizer.copernicus.org/EGU2020/EGU2020-2649.htmlae974a485f413a2113503eed53cd6c53
10.5194/egusphere-egu2020-2649
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Except where otherwise noted, this item's license is described as © Author(s) 2020. This work is distributed under the Creative Commons Attribution 4.0 License.