Introducing a simplified rock physics model to estimate shear velocity to consider the geometry of pore spaces and minerals

Estimation of the reservoir parameters such as porosity, lithology and saturation from well logs and seismic data would require the shear wave velocity data. The high cost of shear wave well logging and subsequently the lack of the shear velocity log in most wells are the main reasons to estimate the shear velocity log. The routine methods for estimating the shear velocity are empirical relations, regression analysis, intelligent methods and rock physics models. Most of these methods do not include the impact of geometry of pore spaces and minerals (aspect ratio). Excluding geometry of pore spaces and minerals from the estimation procedure would create considerable estimation error in the oil and gas reservoirs, particularly in the carbonates. In the first step, shear velocity log is estimated using empirical relations, regression analysis and neural network methods. The estimated shear velocity log using neural network is better than empirical and regression methods. Subsequently, a new simple equation utilizing rock physics models is developed to estimate the shear velocity log. The Xu–White (Geophys Prospect 43: 91–118, 1995) and as reported by Gassmann (Elasticity of porous media, Vierteljahrsschrde Naturforchenden, Gesellschaft, 1951) relations were employed to include the impact of lithology, fluid, minerals and pore spaces. The estimated shear velocity log using new equation compares well with the original as reported by Gassmann (Elasticity of porous media, Vierteljahrsschrde Naturforchenden, Gesellschaft, 1951) and Xu–White (Geophys Prospect 43: 91–118, 1995) rock physics methods. The average difference between the measured and estimated velocity logs using new method is 12.6 m/s (or 0.41%). This equation includes the impact of major parameters in the rock physics model in the simple form; thus, it would be applicable in the carbonate and clastic reservoirs.