Convolutional neural network long short-term memory deep learning model for sonic well log generation for brittleness evaluation

Abstract

Brittleness index, usually calculated by P- and S-wave velocities, is an important parameter used to optimize the sweet spots of shale oil. Empirical relationships or artificial intelligence networks can predict sonic logs based on conventional logging data, but accuracy is limited by the formation types and properties, such as shale sandstone interbeds. Therefore, we propose a hybrid convolutional neural network long short-term memory (CNN-LSTM) deep learning model for the prediction of compressional and shear traveltimes. The new model can extract nonlinear features as well as fluctuating trends of log response features with depth, which is different from most machine learning methods that only consider extracting spatial features between logs or only time-series features of log data sets. We conclude that the new CNN-LSTM network has the highest prediction accuracy (92% and 91.3%) and advantages in predicting curve mutation points compared with other machine learning models. We apply the predicted compressional and shear sonic times for evaluating brittleness to reduce the risk of exploration in shale oil reservoirs.