A Sand-to-Shale Ratio Prediction Method for Variogram Optimization by Constructing Virtual Wells Using Seismic Attributes

Aiming at the low accuracy of variograms caused by sparse and uneven drilling data in traditional sand-to-shale ratio prediction, this paper proposes a sand-to-shale ratio prediction method that integrates geostatistics, seismic geophysical technologies, and the concept of virtual well modeling. With geostatistics as the core, this method achieves accurate prediction of reservoir sand-to-shale ratio through a comprehensive workflow, including basic data preparation, seismic attribute extraction and optimization, virtual well construction, variogram optimization, and Gaussian random function simulation. Taking the upper member of the Pinghu Formation in the study area as a case study, the results indicate that the RMS attribute exhibits the strongest correlation with the sand-to-shale ratio. The variogram optimized using virtual well data exhibits a spherical model, where the major range and minor range are in good agreement with the distribution characteristics of geological bodies within the study area.), Gaussian random function simulation was performed with actual drilling data as hard constraints and seismic attributes as soft constraints. The generated planar distribution map of sand-to-ground ratio aligns well with the sedimentary facies pattern of "higher in the northwest and lower in the southeast" and demonstrates superior detail accuracy compared to traditional methods.. The detailed accuracy of this map outperforms that of traditional methods, providing reliable support for reservoir evaluation and development scheme design.