Data-driven shear wave velocity prediction method via a deep learning based deep CGRU fusion network

The shear-wave velocity (Vs) is a fundamental parameter in geophysical analysis, prestack seismic inversion, and reservoir prediction. For various reasons, the availability of directly measured values of Vs is low, especially in old wells. Therefore, indirect estimations of Vs data on the basis of available reservoir information are important, and the development of a high-efficiency and low-cost prediction method is necessary. We develop a novel prediction method that combined the convolutional neural network (CNN) and gated recurrent unit (GRU) algorithms, based on a deep convolutional GRU (DCGRU) approach. More specifically, a CNN structure was used to identify and memorize the complex relationship between Vs and well log data, whereas a GRU network was introduced to extract key features of the data series in the depth direction. Owing to its structure, the DCGRU approach can seamlessly account for data trends with depth, local correlations across data series, and the actual depth accumulation effect. This approach was tested on datasets from an actual reservoir; it provided more reliable and accurate Vs predictions not only compared to empirical models, but also compared to the CNN and GRU algorithms, applied separately. The proposed approach has potential for accurately estimating Vs from log data. © 2021 Society of Exploration Geophysicists