基于稀疏贝叶斯的分频蚂蚁追踪技术在裂缝刻画中的应用

Application of frequency division ant tracking technology based on Sparse Bayesian in fracture characterization

在裂缝预测方法中,分频处理是常用的裂缝预测方法之一,这对时频分析方法的抗噪性和分辨率都有很高的要求。这里用稀疏贝叶斯方法(Sparse Bayesian Learning,SBL)对地震信号进行子波重构,该方法在地震模型设置时,将噪声模型考虑其中,再用威格纳分布(Wigner-ville Distribution,WVD)进而得到地震信号的时频分布,称之为稀疏贝叶斯时频分析方法(SBL-WVD)。该方法不仅有很好的抗噪性,还拥有较高的时频分辨率。考虑到高频相干对中小尺度裂缝带预测效果较好,而蚂蚁追踪技术对裂缝地刻画更清晰,因此采用分频相干以及蚂蚁追踪这种综合方法对裂缝进行预测。应用到绥中某工区发现:该方法对中小尺度裂缝刻画清晰,在实际应用中取得良好的效果,为后期的储层识别提供借鉴。

In the fracture prediction methods frequency division processing is one of the commonly used fracture prediction methods,which has high requirements for the noise resistance and resolution of time-frequency analysis methods.This paper uses sparse Bayesian learning(SBL)to reconstruct the wavelet of seismic signals.When setting the seismic model,the noise model is considered,and then the time-frequency distribution of the seismic signal is obtained by Wigner Ville distribution(WVD).This paper is called sparse Bayesian time-frequency analysis method(sbl-wvd).This method has not only good noise resistance and high time-frequency resolution.High-frequency coherence has a better prediction effect on small and medium-sized fracture zones.Furthermore,ant-tracking technology has a more precise description of fractures.Therefore,the comprehensive frequency division coherence and ant trackingis used to predict fractures.When applied to a work area in Suizhong,it is found that this method can predict small and medium-sized fractures.The fracture description is clear,which has achieved good results in practical application and laid a foundation for reservoir identification in the later stage.