Predicting Shear Wave Velocity Using a Convolutional Neural Network and Dual-Constraint Calculation for Anisotropic Parameters Incorporating Compressional and Shear Wave Velocities

Abstract

As the exploration of unconventional reservoirs progresses, characterizing challenging formations like tight sandstone becomes increasingly complex. Anisotropic parameters play a vital role in accurately characterizing these unconventional reservoirs. In light of this, this paper introduces an approach that uses a dual-constraint anisotropic rock physics model based on compressional and shear wave velocities. The proposed method aims to enhance the precision of anisotropic parameter calculations, thus improving the overall accuracy of reservoir characterization. The paper begins by applying a convolutional neural network (CNN) to predict shear wave velocity, effectively resolving the issue of incomplete shear wave logging data. Subsequently, an anisotropic rock physics model is developed specifically for tight sandstone. A comprehensive analysis is conducted to examine the influence of quartz, clay porosity aspect ratio, and fracture density on compressional and shear wave velocities. Trial calculations using the anisotropic model data demonstrated that the accuracy of calculating anisotropic parameters significantly improved when both compressional and transverse wave velocity constraints were taken into account, as opposed to relying solely on the compressional wave velocity constraint. Furthermore, the rationality of predicting anisotropic parameters using both the shear wave velocity predicted by the convolutional neural network and the measured compressional wave velocity was confirmed using the example of deep tight sandstone in the Junggar Basin.