The case study: Estimation of shear wave velocity in carbonate reservoir

Summary: P-wave (Vp) and S-wave (Vs) velocities are the main factors controlling seismic responses. These two velocities are dependent on several factors like lithology, pore fluids, pressure, temperature etc. Each of these factors may affect either of these velocities in a unique way, and the final measured velocity is the net effects resulting from all of these factors together. Therefore, the relationship between these velocities is a good indicator of intrinsic and/or extrinsic rock properties and plays an important role is seismic characterization techniques like Amplitude Versus Offset (AVO) (Castagna et al. 1993) to determine rock properties like: lithology or even pore fluid. Laboratory measurements in this regards can provide useful information on the reason for variations in seismic velocities with changing rock properties for the purpose of seismic characterization studies.Introduction: Shear wave velocity has a wide range application in seismic reservoir characterization and one of its main applications is in seismic inversion studies. However, this information is not always available for a large number of exploration and production wells. Therefore, sophisticated methods for modeling shear wave velocity can play an important role in accuracy of the subsurface studies. In recent years some numerical and physical models are proposed to estimate shear wave velocity.Methodology and Approaches: In this study, we compare application of empirical relationships, artificial neural networks and rock physics modeling to estimate shear wave velocity. Furthermore, the results of these different approaches are compared against each other.Results and Conclusions: The results show that the accuracy of shear wave estimation reduces from rock physics models to artificial neural networks and finally to experimental relationships. This means that using rock physics modeling for shear wave velocity estimation can be suited better for seismic inversion purposes as they can better present changes in geology with respect to other methods.

Keywords:

Shear wave , Empirical relations , Artificial Neural Network , Rock Physics1Corresponding