Abstract:Seismic emission tomography (SET) is a hypocentral location method suitable for surface microseismic monitoring. This method makes use of the monitoring signals from many stations on the ground to image a specific area of a reservoir layer by layer. The images are used to determine whether there are microseismic events and where the hypocenter coordinates are. In traditional processing methods, whether the SET images of a section of signal contain an effective microseismic event is usually judged by human experiences. It is difficult to process all the massive monitoring data manually, and thus it cannot make full use of the advantages of the SET method. To solve this problem, a residual network is proposed to process SET images of microseismic monitoring data, which can detect microseismic events automatically. Firstly, a large number of SET images are produced using synthetic data and actual surface microseismic monitoring data of an oil well with hydraulic fracturing, and these SET images are used to construct a sample data set for training and testing a residual network. In this way, a residual network model with the highest accuracy of event detection is obtained. Then, the trained residual network model is employed to detect and locate microseismic events from other SET images produced by synthe-tic signals with different signal-to-noise ratios and surface microseismic monitoring data of other oil & gas wells with hydraulic fracturing. The test results prove that the proposed method can detect microseismic events effectively and has good noise suppression and generalization abilities.
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