Premium
Quantifying Fracture Networks Inferred From Microseismic Point Clouds by a Gaussian Mixture Model With Physical Constraints
Author(s) -
McKean S. H.,
Priest J. A.,
Dettmer J.,
Eaton D. W.
Publication year - 2019
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1029/2019gl083406
Subject(s) - microseism , hydraulic fracturing , induced seismicity , geology , cluster analysis , point process , fracture (geology) , seismology , slip (aerodynamics) , petroleum engineering , geotechnical engineering , computer science , machine learning , statistics , physics , mathematics , thermodynamics
Microseismicity is generated by slip on fractures and faults and can be used to infer natural or anthropogenic deformation processes in the subsurface. Yet identifying patterns and fractures from microseismic point clouds is a major challenge that typically relies on the skill and judgment of practitioners. Clustering has previously been applied to tackle this problem, but with limited success. Here, we introduce a probabilistic clustering method to identify fracture networks, based on a Gaussian mixture model algorithm with physical constraints. This method is applied to a rich microseismic data set recorded during the hydraulic fracturing of eight horizontal wells in western Canada. We show that the method is effective for distinguishing hydraulic‐fracture‐created events from induced seismicity. These fractures follow a log‐normal distribution and reflect the physical mechanisms of the hydraulic fracturing process. We conclude that this method has wide applicability for interpreting natural and anthropogenic processes in the subsurface.