摘要
裂缝密度是定量预测裂缝性储层的重要参数之一。Varela利用方位角上的反射系数,运用SVD方法反演HTI介质裂缝密度。笔者将不同方位角测线中两两相交测线的反射系数做差,对反射系数差值运用SVD方法反演HTI介质裂缝密度,各向异性参数在做差后的反射系数中具有更显著的作用。笔者首先针对2种方法的基本原理进行了阐述,然后进行数值计算,并对反射系数存在随机扰动情况下2种方法的反演结果进行对比,最后对笔者提出的改进方法进行稳定性分析。反演结果显示:噪声较小时,2种方法的反演结果都接近真实值,然而当增大噪声时,改进的方法比原方法反演结果更好,因此改进的方法比原方法更具有抗噪能力;随着相交测线夹角的增大,改进的方法受随机扰动影响的稳定性增强,在方位正交时受随机扰动的影响最小;纵波高信噪比AVA数据越多,反演的结果越稳定。
Crack density is one of the important parameters which can be applied to the quantitative prediction of crackd reservoirs. Varela inverted crack density using SVD (singular value decomposition) method based on reflection coefficients various azimuth for HTI (horizontal transverse isotropy) media. We invert the crack density of HTI media by using SVD method based on difference of PP-wave AVA (amplitude variation with azimuth) data from crossing seismic survey lines. Anisotropy parameters play a more important role in the reflection coefficients difference. Firstly, the main principles of the two methods are described. Then the numerical computation was carried out and the inversion results of the two methods when reflection coefficients have random disturbances were contrasted. Finally, the stability analyses of the new improved method had made. The results show that the inversion results of both of these two methods are approaching to real value. However, the result of the improved method is better than that one of the old method, so the improved method has better anti-noise capabilities. The stability of this method goes better when the difference of azimuth increases. The influence caused by random disturbances is lowest when seismic survey lines are orthogonal achieve a good stability when PP wave AVA data are with high signal to
出处
《吉林大学学报(地球科学版)》
EI
CAS
CSCD
北大核心
2013年第5期1655-1662,共8页
Journal of Jilin University:Earth Science Edition
基金
国家自然科学基金项目(40974054)
国家"973"计划项目(2009CB219301)
油页岩勘探开发利用产学研用合作创新研究项目(OSP-02
OSR-02)
公益性行业科研专项项目(201011078)
关键词
裂缝密度
振幅随方位角变化
奇异值分解
HTI介质
crack density
amplitude variation with azimuth(AVA) (SVD)
HTI media intersection. The results can noise ratio.
singular value decomposition