Imaging discrete fracture networks using the location and moment tensors of microseismic events

Abstract

Microseismic monitoring provides important information on the locations and moment tensors of microseismic events, and this information can be used to understand the behaviour of the fractures more completely. Characterisations of fractures are used to investigate flow paths and for noninvasive investigation of shale gas sites, geothermal developments, and radioactive waste storage/disposal sites. The locations of microseismic events can be used to estimate the geometry and distribution of fractures and faults, and the moment tensors provide information on the orientations of fracture planes and event magnitudes. We propose an effective fracture network imaging method using microseismic event locations and their moment tensors. For this purpose, the conventional Random Sample Consensus (RANSAC) method was improved so that fracture planes could be extracted quickly and efficiently from point cloud data. In addition, through the application of Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to the microseismic event location data and moment tensor information, the accuracies of the locations and the orientations of fracture planes were improved. The developed algorithm was applied to synthetic data, including event locations and noise errors. The results showed that the developed method could extract the fracture planes reliably. Furthermore, the input parameters used in the proposed algorithm were examined, and the results of the conventional RANSAC method and the developed algorithm were compared. Finally, our method was applied to microseismic field data obtained from a shale gas/oil play, and the result accurately depicted the dominant strike of fracture planes.