Hydraulic fracture characterization by integrating multidisciplinary data from the Hydraulic Fracturing Test Site 2 (HFTS-2)

Zhang, Zhishuai; DiSiena, James; Bevc, Dimitri; Ning, Ivan Lim Chen; Tan, Yunhui; Swafford, Laura; Craven, Mike; Hughes, Kelly; Vissotski, Andrea

doi:10.15530/urtec-2021-5039

Cited by 19 publications

(1 citation statement)

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“…Advanced processing techniques using DAS have been developed to locate microseismic events (Verdon et al, 2020) and to determine source mechanisms (Baird et al, 2020;Rodriguez & Wuestefeld, 2020). The results from previous studies highlight the potential to integrate measurements from different fiber-optic cables, including microseismic, strain, and temperature, to improve subsurface monitoring during hydraulic fracturing (Zhang et al, 2021).…”

Section: Open Access Edited Bymentioning

confidence: 99%

Machine learning-assisted processing workflow for multi-fiber DAS microseismic data

Eaton

Igonin

et al. 2023

Front. Earth Sci.

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In recent years, Distributed Acoustic Sensing (DAS) deployed in deviated wells has been increasingly used for microseismic monitoring. DAS can provide observations of microseismic wavefields with high spatial resolution and wide aperture, at the cost of unusually large data volumes compared with conventional downhole microseismic monitoring. To tackle this big-data challenge, we have developed key elements of a processing workflow that is assisted by machine learning techniques. We trained a convolutional neural network (CNN) for event detection and a U-Net model for both P- and S-wave arrival time picking. The workflow was applied to two multiwell DAS datasets acquired during hydraulic fracturing completions in western Canada. These datasets also include co-located 3C borehole geophone arrays that enable further comparison between catalogs from both sensor types. Compared with a traditional short-term average/long-term average (STA/LTA) method for event detection, our results indicate that the CNN method has a lower false-trigger rate and increases the event catalog size by a factor of 2.6–5.6. U-Net yields arrival-time picks with relatively small errors, high efficiency, and minimal user intervention, providing hypocenter location and focal depth that is arguably more accurate than the geophone catalog. While the proposed automated workflow requires substantial effort to build high-quality and large training datasets, it enables the use of DAS for real-time seismicity monitoring and risk management after the training stage. Although the DAS system detected fewer events than the geophone catalog and missed smaller magnitude events, our results indicate that fiber-optic sensors provide enough sensitivity to detect and locate sufficient events to characterize the treatment stages. DAS also captured induced events located at a hypocentral distance of >1 km, which are possibly indicative of reactivation of structural features.

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