Research On Automatic Microseismic Event Detection And Arrival Picking Based On Fast-AIC Algorithm

Microseismic monitoring technology is one of the most reliable methods to evaluate the fracturing effect of oil fields.Based on the picking result of micrseismic signal,we can judge the direction and dimension of the fracture,and analyze the mechanism of the inversion,providing the basis for the subsequent production and development of oil field.The picking of the microseismic signal onset is a key step of the microseismic monitoring technology.The picking speed and accuracy of microseismic arrival time directly affect the efficiency and reliability of microseismic location.Microseismic monitoring system includes borehole monitoring and ground monitoring,Because of microseismic ground monitoring of simple construction,low cost and no monitoring well,will be the main way of microseismic monitoring of the oil industry and other areas in the future.Due to the location of the microseismic source occurred in the hundreds or even thousands of meters underground,and the ground noise interference is larger,which leads to the fact that the existing arrival picking methods of microseismic signal have some limitations in dealing with the actual microseismic data.Therefore,in this paper,the microseismic event identification and the arrival picking technology are studied based on the characteristics of microseismic monitoring data.The number of microseismic events induced during hydraulic fracturing and the time of induction are uncertain.In order to accurately pick all the arrival time of microseismic signal,we must determine the number of microseismic events and the general induced time first,and then select the local microseismic data for accurately picking the arrival time of micoroseismic signal.For the microseismic event automatic identification method,the conventional methods are based on single seismograph signal processing.The identifications of microseismic event are often missed,appear false inspection situation,and can’t guarantee the number of microseismic events recorded by each seismograph in the identification is the same.The Akaike Information Criteria(AIC)method has the advantages of simple realization and easy calculation,and is suitable for local data picking with microseismic events.However,for the long-term recording of microseismic data processing,its computational efficiency needs to be improved.Aiming at the shortcomings in the above research,this paper proposed automatic microseismic event detection and arrival picking based on Fast-AIC algorithm.Firstly,in order to avoid the defects of missed and false detection in conventional microseismic event detection methods based on single-channel microseismic data,a microseismic event recognition method based on perforating signal is proposed.The whole microseismic data are corrected and superimposed by the perforation signal,and the microseismic events are identified by the high SNR model trace obtained after the superposition.At the same time,in order to overcome the dependence of the microseismic detection method on perforating data based on the perforating signal,In this paper,when the actual perforation data is not found,the layered velocity model is established according to the acoustic logging curve.The ray tracing theory is used to simulate the perforating signal instead of the actual perforation signal.Then,according to the parameters such as the parameters of the detector of the hydraulic fracturing monitoring system and the formation speed,local effective data are selected near the microseismic event trigger time.Curvelet transform is used to filter the local data and provide local data with high signal-to-noise ratio for accurate picking of microseismic signals.Finally,in order to improve the picking efficiency of microseismic signals,the traditional AIC algorithm is deduced mathematically.The original formula is transformed to obtain the linear combination of arithmetic and squared sums of discrete real numbers.We can reduce the repeated calculation and get fast AIC algorithm(Fast-AIC algorithm)in this way.When the data length exceeds 6500 sampling points,the computational efficiency is improved by more than 1000 times compared with the traditional AIC method.Based on the technical scheme of the paper,we program the automatic picking method of microseismic signals based on Fast-AIC algorithm.The synthetic data,and a lot of experiments are carried out to analyze the reliability and accuracy of the method.At the same time,the proposed method and several conventional methods are used to pick the arrival time of the actual hydraulic fracturing microseismic data in Shanxi Province,China.Compared with the artificial picking results,the absolute picking error is smaller.