利用谱分解技术预测河流相储层

River sedimentary microfacies prediction based on spectral decomposition

利用谱分解技术对原始地震数据体进行相应的数学变换可得到调谐体、时频体和单频体(振幅体和相位体),进而通过对调谐体、时频体和单频体的解释得到对目标体的地质认识。本文应用最大熵法定量求取储层厚度,并对误差进行分析,同时运用频率扫描方法定性预测储层厚度变化,并利用广义S变换方法和交会融合预测沉积微相。首先,通过多井对比,分析测井相和砂体厚度;然后通过井震结合,分析各井对应井段的薄层砂岩振幅调谐体,确定合理的调谐频率,并对砂体厚度进行分析;再通过建立响应频率、砂体厚度与沉积微相之间的交会关系,在测井微相约束下预测沉积微相。应用结果证实,谱分解技术结合井资料可直观地反映河道砂体储层厚度分布和沉积微相区带展布规律。

Spectrum decomposition uses mathematical transformation to get tuning cube,time-frequency volume and single frequency volume(amplitude and phase)from seismic data.Maximum entropy method is applied in this article to calculate reservoir thickness,and the error is analyzed.At the same time the frequency scanning method is used to predict reservoir thickness and GST and RGB are used to predict sedimentary microfacies.First the log facies and sand body thickness are analyzed.Then the tuning amplitude of thin layer sandstones is used to determine reasonable tuning frequencies and analyze sand body thickness.Finally crossplots among response frequency,sand thickness,and sedimentary microfacies are built to predict the sedimentary microfacies under the restriction of well logging microfacies with loggingmicrofacies constrain.Application results confirm that the spectral decomposition combined with well data can intuitively reflect channel sand reservoir thickness and sedimentary microfacies belt distribution.