Induced seismicity is a significant concern during fluid injection projects
such as hydraulic fracturing for shale gas, enhanced geothermal systems and
wastewater injection. With downhole microseismic monitoring, operators can
obtain large seismic datasets to detect hydraulic fracturing induced seismicity
(HFIS). Deep learning models like convolutional neural networks (CNNs)
can offer rapid event detection in these large datasets. Rapid event detection
can be useful for risk management strategies. CNNs have already displayed
success in detecting regional earthquakes. Here, we examined whether a
CNN pre-trained on regional earthquakes can also detect HFIS within high
frequency continuous downhole data. We used data from a shale gas site at
Preston New Road, UK, to assess the CNN model. The catalogue of the site,
which contains over 23,000 events (-2.839 ≤ Mw ≤ 1.155), was generated
using the coalescence microseismic mapping (CMM) method. Using confusion
matrices, we evaluated the model’s ability to pick P and S-phases on single
stations. To assess multi-station performance, we compared event catalogues
and locations determined by the model and CMM method. We found that
model performance declines with decreasing Mw. The model often misses
small Mw < -2 events but detects new events not previously catalogued (230
new events within one hour). The model detected many new events during
periods of high seismicity during injection. We infer that the CMM catalogue
is more complete during less seismically active periods as the CNN model
did not detect many new events. This study indicates that the pre-trained
CNN offers the potential of detecting most events that the CMM detects
(87.7%) in addition to more events during very active periods. The CNN
produces these results more efficiently so it is promising, however, it requires
further retraining with a dataset that represents the HFIS to improve phase
detection and accurate picking.
Date of Award | 21 Jan 2021 |
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Original language | English |
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Awarding Institution | |
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Supervisor | Max Werner (Supervisor) |
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- Earthquakes
- Machine Learning
- Seismology
- Observational Seismology
- Hydraulic Fracturing
- Induced Seismicity
Using deep learning for phase detection and event location on hydraulic fracturing-induced seismicity
Lim Shin Yee, C. (Author). 21 Jan 2021
Student thesis: Master's Thesis › Master of Science by Research (MScR)