A Novel Method of Deep Learning for Shear Velocity Prediction in a Tight Sandstone Reservoir

Abstract

Shear velocity is an important parameter in pre-stack seismic reservoir description. However, in the real study, the high cost of array acoustic logging leads to lacking a shear velocity curve. Thus, it is crucial to use conventional well-logging data to predict shear velocity. The shear velocity prediction methods mainly include empirical formulas and theoretical rock physics models. When using the empirical formula method, calibration should be performed to fit the local data, and its accuracy is low. When using rock physics modeling, many parameters about the pure mineral must be optimized simultaneously. We present a deep learning method to predict shear velocity from several conventional logging curves in tight sandstone of the Sichuan Basin. The XGBoost algorithm has been used to automatically select the feature curves as the model’s input after quality control and cleaning of the input data. Then, we construct a deep-feed neuro network model (DFNN) and decompose the whole model training process into detailed steps. During the training process, parallel training and testing methods were used to control the reliability of the trained model. It was found that the prediction accuracy is higher than the empirical formula and the rock physics modeling method by well validation.