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Shear Wave Splitting Analysis to Estimate Fracture Orientation and Frequency Dependent Anisotropy

Gholami R., Moradzadeh A., Rasouli V., Hanachi J.

Acta Geophysica

2016

Vol. 64, no. 1

76--100

Shear wave splitting is a well-known method for indication of orientation, radius, and length of fractures in subsurface layers. In this paper, a three component near offset VSP data acquired from a fractured sandstone reservoir in southern part of Iran was used to analyse shear wave splitting and frequency-dependent anisotropy assessment. Polarization angle obtained by performing rotation on radial and transverse components of VSP data was used to determine the direction of polarization of fast shear wave which corresponds to direction of fractures. It was shown that correct implementation of shear wave splitting analysis can be used for determination of fracture direction. During frequencydependent anisotropy analysis, it was found that the time delays in shearwaves decrease as the frequency increases. It was clearly demonstrated throughout this study that anisotropy may have an inverse relationship with frequency. The analysis presented in this paper complements the studied conducted by other researchers in this field of research.

Shear wave velocity prediction using seismic attributes and well log data

Gholami R., Moradzadeh A., Rasouli V., Hanachi J.

Acta Geophysica

2014

Vol. 62, no. 4

818--848

Formation’s properties can be estimated indirectly using joint analysis of compressional and shear wave velocities. Shear wave data is not usually acquired during well logging, which is most likely for cost saving purposes. Even if shear data is available, the logging programs provide only sparsely sampled one-dimensional measurements: this information is inadequate to estimate reservoir rock properties. Thus, if the shear wave data can be obtained using seismic methods, the results can be used across the field to estimate reservoir properties. The aim of this paper is to use seismic attributes for prediction of shear wave velocity in a field located in southern part of Iran. Independent component analysis (ICA) was used to select the most relevant attributes to shear velocity data. Considering the nonlinear relationship between seismic attributes and shear wave velocity, multi-layer feed forward neural network was used for prediction of shear wave velocity and promising results were presented.