Two methods commonly used in microseismic event detection during hydraulic fracturing:short term averaging/long term averaging(STA/LTA)algorithm based on energy and template matching based on waveform similarity,have the problems of low accuracy and low speed of detection,respectively.Thus,we establish a technique which integrates fingerprinting and similar-ity thresholding(FAST)with homomorphic deconvolution for noise reduction,STA/LTA,and density-based spatial clustering of applications with noise(DBSCAN)for fast microseismicity detection with high precision.After microseismic data denoising through homomorphic deconvolution and STA/LTA detection of microseismic events with high signal-to-noise ratios which will be taken as the templates,we use FAST for template and continuous waveform fingerprinting and then examine the Jaccard similarity of fingerprints to check out those events with low signal-to-noise ratios and obtain first arrivals of P-waves at each station.DB-SCAN is finally utilized for the correlation of the same microseismic facies at different stations to eliminate false events.According to the model study,we successfully detect all the 171 synthetic microseismic events with different signal-to-noise ratios using our method.We also compare our method with template matching and STA/LTA in handling microseismic data acquired from the 19th stage of a horizontal well fractured on November 10 2014 for shale gas production in Weiyuan,the Sichuan Basin.Our method is su-perior to STA/LTA in the detection of microseismic events with low signal-to-noise ratios and template matching in computational efficiency.
国家科技期刊平台
NSTL
维普
万方数据
