A strategy for automated analysis of passive microseismic data to image seismic anisotropy and fracture characteristics

Abstract

ABSTRACTMonitoring of induced seismicity is gaining importance in a broad range of industrial operations from hydrocarbon reservoirs to mining to geothermal fields. Such passive seismic monitoring mainly aims at identifying fractures, which is of special interest for safety and productivity reasons. By analysing shear‐wave splitting it is possible to determine the anisotropy of the rock, which may be caused by sedimentary layering and/or aligned fractures, which in turn offers insight into the state of stress in the reservoir. We present a workflow strategy for automatic and effective processing of passive microseismic data sets, which are ever increasing in size. The automation provides an objective quality control of the shear‐wave splitting measurements and is based on characteristic differences between the two independent eigenvalue and cross‐correlation splitting techniques. These differences are summarized in a quality index for each measurement, allowing identification of an appropriate quality threshold. Measurements above this threshold are considered to be of good quality and are used in further interpretation. We suggest an automated inversion scheme using rock physics theory to test for best correlation of the data with various combinations of fracture density, its strike and the background anisotropy. This fully automatic workflow is then tested on a synthetic and a real microseismic data set.