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Speaker: Yuanyuan Ma, Dept of Geoscience, University of Calgary
Co-Authors: David W. Eaton, Chaoyi Wang, Dept of Geoscience, University of Calgary

Distributed acoustic sensing (DAS) is a rapidly developing technology that can be used for characterizing the geometry and propagation of hydraulic fractures. DAS provides observations of microseismic wavefields with high spatial resolution, which contain direct P- and S-arrivals as well as converted and reflected waves. In addition to traditional approaches for microseismic event location and source mechanism analysis, their wide aperture and high spatial resolution means that DAS recordings can be used to image induced fractures using reflected waves. The reflections may be generated by nearby hydraulic fractures from earlier treatment stages or small pre-existing faults. We use a straightforward method based on f-k filtering and ray-tracing to map reflected S-waves from the data domain to the model domain. The presentation will demonstrate the machine learning-assisted processing workflow for DAS microseismic and the detailed fracture imaging approach using field data from the Montney formation in western Canada. The results of fracture imaging indicate fracture development that is consistent with fracture-driven interactions (FDIs) interpreted from low-frequency DAS (LF-DAS) data, as well as distant fractures that do not reach the fiber and thus are not directly observed by LF-DAS. Fracture images using reflections produced by several microseismic events during the same stage provides the opportunity to observe snapshots of dynamic fracture evolution processes. 

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