Unsupervised learning tracks spatiotemporal evolution of hydraulic
fractures
- Aditya Chakravarty,
- Siddharth Misra
Abstract
Enhanced geothermal systems can provide a substantial share of the
global energy demand if certain hurdles are overcome. One such hurdle is
the accurate imaging of the fracture networks created in subsurface
through hydraulic stimulation of these systems. Microseismicity
associated with the stimulation is the primary means to locate the event
hypocenters for estimating the stimulated rock volume. Using the data
from a single three-component accelerometer in a monitoring well, the
polarization features viz. azimuth, incidence, rectilinearity, and
planarity are used as inputs for the unsupervised manifold approximation
followed by clustering. We show that density-based clusters in the
projected 3D space correspond to distinct types of hydraulically
fractured zones around the injection point, thereby refining the
interpretation of the microseismic cloud. The temporal evolution of
these clusters can be used to track fracture creation and propagation
for the various types of fracture zones.