AVO analysis using horizontal component of seismic data in equivalent offset migration method

Tomoaki Tanaka, H. Mikada, J. Takekawa
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Abstract

For exploring subsurface resources such as oil or natural gas reservoirs, seismic reflection survey has been widely iplementd in order to image subsurface structures. In recent years, utilization of S-wave or converted wave is required for estimating lithology or petrophysical properties of reservoir rock. However, such an analysis of S-wave seismograms had been relatively difficult. On the other hand, equivalent offset migration (EOM) is one of the prestack time migrations and has been found to be effective method for imaging S-wave information on the common scatter point (CSP) gather with recorded horizontal component in our previous study. Furthermore, S-wave AVO effect has also been confirmed by the amplitude reversal of S-wave event on the CSP gathers. Therefore, we propose the procedure of accurate estimation of densities and shear modulus with S-wave source. First, we conduct numerical experiment with a 2D layer model using horizontal point force to obtain horizontal component seismic data, in which we can get higher S/N data about S-wave. Second, we implement EOM with those data to get CSP gather, and calculate each cross-correlated value versus incident angle as observed waveform information. Third, in contrast, we generate calculated waveform information as a function of incident angle and physical properties with geometrical spreading, radiation pattern and S-wave reflection coefficient. Finally, we can estimate the optimal solutions by minimizing the misfit from the both information.
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等效偏移偏移法中地震资料水平分量AVO分析
在油气等地下资源勘探中,为了对地下构造进行成像,地震反射测量已得到广泛应用。近年来,需要利用横波或转换波来估计储层岩石的岩性或岩石物理性质。然而,这种对s波地震记录的分析相对困难。另一方面,等效偏移偏移(EOM)是叠前时间偏移的一种,在前人的研究中发现了在记录水平分量的共散射点(CSP)集上成像s波信息的有效方法。此外,s波事件在CSP集上的振幅反转也证实了s波AVO效应。因此,我们提出了用s波源精确估计密度和剪切模量的方法。首先,利用水平点力进行二维层模型数值实验,获得水平分量地震数据,得到S波高信噪比数据;其次,我们利用这些数据实现EOM,得到CSP集合,并计算每个相互相关值与入射角作为观测波形信息。第三,我们生成了计算波形信息,作为入射角和几何扩展、辐射方向图和s波反射系数的物理性质的函数。最后,我们可以通过最小化这两个信息的不匹配来估计最优解。
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