Abstract
Shear and compressional wave velocities, coupled with other petrophysical data, are very important for hydrocarbon reservoir characterization. In situ shear wave velocity (Vs) is measured by some sonic logging tools. Shear velocity coupled with compressional velocity is vitally important in determining geomechanical parameters, identifying the lithology, mud weight design, hydraulic fracturing, geophysical studies such as VSP, etc. In this paper, a correlation between compressional and shear wave velocity is obtained for Gachsaran formation in Maroon oil field. Real data were used to examine the accuracy of the prediction equation. Moreover, the genetic algorithm was used to obtain the optimal value for constants of the suggested equation. Furthermore, artificial neural network was used to inspect the reliability of this method. These investigations verify the notion that the suggested equation could be considered as an efficient, fast, and cost-effective method for predicting Vs from Vp.
Highlights
Compressional and shear velocities (Vp and Vs, respectively) are important in seismic inversion and petrophysical evaluation of formations, especially for analysis of reservoir geomechanical properties
If sonic tools to measure VS are not available, we may use a prediction equation for estimating shear wave velocity based on compressional wave velocity obtained from monopole sonic log (Liu et al 2012)
As it is obvious from petrophysical logs and geological studies, Gachsaran formation in Maroon oil field consist of seven members as presented in Table 1 (Memari 2013)
Summary
Compressional and shear velocities (Vp and Vs, respectively) are important in seismic inversion and petrophysical evaluation of formations, especially for analysis of reservoir geomechanical properties. If sonic tools to measure VS are not available, we may use a prediction equation for estimating shear wave velocity based on compressional wave velocity obtained from monopole sonic log (Liu et al 2012). Almost all such equations are empirical (Asef and Farrokhrouz 2010). The present study was conducted to develop a reliable model for predicting shear wave velocity in a rock, based on compressional wave velocity in Maroon oil field For this purpose, we utilized different regression models to obtain the most appropriate approach. We applied a neural network model to examine the accuracy and reliability of the suggested approach
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