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Unsupervised learning from three-component accelerometer data to monitor the spatiotemporal evolution of meso-scale hydraulic fractures

Journal Article · · International Journal of Rock Mechanics and Mining Sciences
 [1];  [2]
  1. Texas A & M Univ., College Station, TX (United States); OSTI
  2. Texas A & M Univ., College Station, TX (United States)
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Enhanced geothermal systems can provide a substantial share of the global energy demand. There exist several hurdles in the engineering implementations of such geothermal systems. One such hurdle is the accurate monitoring of the fracture networks created in subsurface through hydraulic stimulation of these systems. Micro seismicity associated with the stimulation is the primary means to locate the event hypocenters for estimating the stimulated rock volume. Existing methods for location the hypocenters are restricted to only the highest amplitude impulsive signals that are simultaneously detected on several sensors. Consequently, a large portion (usually ~99%) of the measurements are left unused. In this paper, an unsupervised manifold-approximation followed by clustering of 3-component accelerometer data is used to analyze the seismicity recorded on a monitoring well. With this method, a larger portion of the measured signal is used for the monitoring of the hydraulic fracture network. We analyze the EGS Collab experiment 1 microseismic data, recorded at the Sanford Underground Research Facility, South Dakota. Using the data from a single three-component accelerometer, the polarization features viz. Azimuth, incidence, rectilinearity, and planarity are used as inputs for the unsupervised manifold approximation followed by clustering. Our study shows that density-based clusters in the projected 3D space correspond to distinct types of hydraulically fractured zones around the injection point. Finally, we show that the temporal evolution of these clusters can be used to track fracture creation and propagation.
Research Organization:
Texas A & M Univ., College Station, TX (United States). Texas A & M Engineering Experiment Station
Sponsoring Organization:
USDOE Office of Science (SC), Basic Energy Sciences (BES). Chemical Sciences, Geosciences & Biosciences Division (CSGB)
Grant/Contract Number:
SC0020675
OSTI ID:
1977218
Journal Information:
International Journal of Rock Mechanics and Mining Sciences, Journal Name: International Journal of Rock Mechanics and Mining Sciences Journal Issue: C Vol. 151; ISSN 1365-1609
Publisher:
ElsevierCopyright Statement
Country of Publication:
United States
Language:
English

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Related Subjects

58 GEOSCIENCES
Engineering
Enhanced Geothermal System (EGS)
Feature engineering
Hydraulic fracturing
Induced seismicity
Micro-seismic monitoring
Mining & Mineral Processing
Unsupervised learning