Machine learning-assisted processing workflow for multi-fiber DAS microseismic data
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 con...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-02-01
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| Series: | Frontiers in Earth Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/feart.2023.1096212/full |
| _version_ | 1811170116400840704 |
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| author | Yuanyuan Ma David Eaton Nadine Igonin Chaoyi Wang |
| author_facet | Yuanyuan Ma David Eaton Nadine Igonin Chaoyi Wang |
| author_sort | Yuanyuan Ma |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-10T16:51:59Z |
| format | Article |
| id | doaj.art-6272f9a04aeb45839b8b6b6117a62804 |
| institution | Directory Open Access Journal |
| issn | 2296-6463 |
| language | English |
| last_indexed | 2024-04-10T16:51:59Z |
| publishDate | 2023-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Earth Science |
| spelling | doaj.art-6272f9a04aeb45839b8b6b6117a628042023-02-07T14:09:28ZengFrontiers Media S.A.Frontiers in Earth Science2296-64632023-02-011110.3389/feart.2023.10962121096212Machine learning-assisted processing workflow for multi-fiber DAS microseismic dataYuanyuan Ma0David Eaton1Nadine Igonin2Chaoyi Wang3Department of Geoscience, University of Calgary, Calgary, AB, CanadaDepartment of Geoscience, University of Calgary, Calgary, AB, CanadaBureau of Economic Geology, Jackson School of Geosciences, The University of Texas at Austin, Austin, TX, Unites StatesDepartment of Geoscience, University of Calgary, Calgary, AB, CanadaIn 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.https://www.frontiersin.org/articles/10.3389/feart.2023.1096212/fulldistributed acoustic sensing (DAS)data analysismicroseismic (MS) monitoringmachine learning (ML)event detectionarrival time picking |
| spellingShingle | Yuanyuan Ma David Eaton Nadine Igonin Chaoyi Wang Machine learning-assisted processing workflow for multi-fiber DAS microseismic data Frontiers in Earth Science distributed acoustic sensing (DAS) data analysis microseismic (MS) monitoring machine learning (ML) event detection arrival time picking |
| title | Machine learning-assisted processing workflow for multi-fiber DAS microseismic data |
| title_full | Machine learning-assisted processing workflow for multi-fiber DAS microseismic data |
| title_fullStr | Machine learning-assisted processing workflow for multi-fiber DAS microseismic data |
| title_full_unstemmed | Machine learning-assisted processing workflow for multi-fiber DAS microseismic data |
| title_short | Machine learning-assisted processing workflow for multi-fiber DAS microseismic data |
| title_sort | machine learning assisted processing workflow for multi fiber das microseismic data |
| topic | distributed acoustic sensing (DAS) data analysis microseismic (MS) monitoring machine learning (ML) event detection arrival time picking |
| url | https://www.frontiersin.org/articles/10.3389/feart.2023.1096212/full |
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