Research On Downhole Microseismic Noise Suppression Algorithm Based On Shearlet Transform And Compressed Sensing

With the continuous growth of world oil and gas demand,oil and gas as non-renewable resources are gradually consumed.And low permeability reservoirs have gradually become an important way to improve oil production.Hydraulic fracturing is an effective evaluation method for exploration of low permeability oilfields.Meanwhile,microseismic monitoring,as a fracturing monitoring method for low permeability oilfields,has attracted much attention.The common microseismic monitoring techniques are ground monitoring and well monitoring.The microseismic monitoring signals in wells have the characteristics of weak energy,low signal-to-noise ratio and high frequency.Improving the signal-to-noise ratio of monitoring records has become an indispensable step for subsequent signal processing.Because of the poor adaptability of traditional filtering methods in processing such characteristic signals,it is very important to design a noise suppression algorithm for micro-seismic signals in wells.Shearlet decomposition is a new multi-scale geometric analysis technique developed in recent years.It is also a near optimal sparse representation of multidimensional functions.In addition,Shearlet decomposition can be associated with multi-resolution analysis,and its continuous form and discrete form have laid a solid foundation for its application.Based on its excellent properties,this paper combines Shearlet decomposition and hard threshold function to suppress the noise of microseismic data in wells,and compares them with wavelet transform and hard threshold function.Because of the superiority of Shearlet decomposition in two-dimensional space,the algorithm effectively improves the signal-to-noise ratio of microseismic data and retains useful signals.However,due to the high frequency and weak energy of microseismic signals in wells,it is difficult to distinguish signals and noise coefficients in Healet domain by simple threshold functions.Therefore,based on the properties of microseismic signals in wells and Shearlet decomposition,this paper proposes a Healet-based sparse decomposition and compressive sensing algorithm.Considering that the concentration of effective signals at different scales is different after Shearlet decomposition of microseismic signals,the energy spectrum and spectrum of coefficients at different scales in the transform domain are first analyzed,and the coefficients are divided into low-frequency scales in the noise concentration and high-frequency scales in the effective signal concentration.For low-frequency scale coefficients,simple threshold processing can meet the requirements;for high-frequency scale coefficients,because of the large signal concentration,compressed sensing algorithm is used to reconstruct coefficients,and adaptive signal-to-noise separation is achieved.On the basis of studying the characteristics of microseismic signals and Shearlet transform in wells,a microseismic noise suppression method based on Healet sparse decomposition and compressed sensing algorithm is constructed.The effectiveness of the improved algorithm is proved by the simulation of microseismic signal and the actual microseismic data test.