# Estimation of Litho-Fluid Facies Distribution from Zero-Offset Acoustic and Shear Impedances
## 3 The Effect of Seismic Anisotropy on Seismic Data  
Seismic anisotropy causes a misinterpretation of seismic data, which affects the final reservoir model. For example, if seismic anisotropy is neglected, a target may be expected at the wrong depth. Another problem is that seismic anisotropy affects the angle-dependent reflectivity calculation, which is further used for prestack seismic applications such as amplitude versus offset (AVO) and elastic impedance (EI). Figure 1 shows a comparison between the isotropic and anisotropic assumptions, which yield significantly different reflectivities at the top of the C-sand reservoir in the Sawan gas field [62]. In addition to that, seismic anisotropy adds uncertainty to prestack seismic inversion, which results in inaccurate rock properties. A common workflow of anisotropic seismic inversion is to obtain the anisotropy parameters and then process seismic data before the inversion process. The anisotropy parameters can be obtained from various types of data. However, the most confident sources of anisotropy quantification are core data. Traditional laboratory ultrasonic measurements of intrinsic anisotropy [63] depend on a limited number of orientations, resulting in inaccurate anisotropy measurements. On the other hand, more effective methods [64; 65; 66; 67] depend on many ray paths having various inclination angles to the bedding plane. A numerical study was applied to ultrasonic measurements according to Markov chain Monte Carlo simulation (MCMC) [68]. The idea of this study was to obtain the probability distribution curves of the anisotropy parameters based on the layering effects of five lithofacies: dry sandstone, wet sandstone, dry carbonate, wet carbonate, and shale. Accordingly, the anisotropy parameters were simulated for each facies class. The Backus averaging method helped calculate the harmonic means of the anisotropy parameters, assuming a horizontally layered inhomogeneous medium [69]. Another source of anisotropy information is the vertical seismic profile (VSP). For instance, one study aimed at obtaining the horizontal slowness (Sx) as well as the vertical slowness (Sz) and then combining them in the vector way to yield the scalar slowness [70,71]. This method results in accurate arrival time and depth values, but it assumes that layers are homogeneous and horizontal, which is not realistic [61]. The Delta parameter was estimated by using both prestack time data and sonic logs [72]. Moreover, a method, called The Empirical Relationship relates the anisotropy parameters to the shale volume (Vsh) that causes the intrinsic anisotropy [73, 74, 75]. The volume fraction and degree of compaction are assumed to be the controlling factors of the orientation of clay minerals and hence the anisotropy parameters. This method is convenient for heterogeneous media because it solves for the intrinsic anisotropy components. However, the problem of obtaining an accurate zero-offset velocity away from wells still exists. Another study considered the intrinsic anisotropy in shale based on an orientation distribution function [76]. This study aimed at modeling anisotropic elastic properties based on two approaches. The first approach was to apply an anisotropic differential effective medium model (DEM) based on the textural information about the kerogen network in kerogen-rich shale. The second approach was to use the DEM model with a compaction-dependent orientation distribution function to study the anisotropy in laminated shale sand rocks. The anisotropic elastic constants were estimated as functions of the fluid-filled porosity and the aspect ratio of the inclusions [77]. Another study introduced an anisotropic dual porosity (ADP) model to forecast the elastic properties of shale sand rocks based on a combination of the anisotropic formulations of self-consistent approximation (SCA), DEM, and anisotropic Gassmann theory [78]. [PERSON] also estimated both Epsilon and Delta from synthetic modeling through some algorithms such as neural networks and regridding inversion [56].