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

Abstract

Brittleness index, usually calculated by compressional and shear wave velocities, is an important parameter used to optimize the sweet pots of shale oil. The empirical relationships or artificial intelligence networks can predict sonic logs based on conventional logging data, but the accuracy is limited by the formation types and properties, such as shale sandstone interbedded. Therefore, we propose a hybrid CNN-LSTM deep learning model that combines convolutional neural network (CNN) and long short-term memory (LSTM) for the prediction of the compressional and shear travel times. 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 consider extracting time-series features of log datasets.We conclude that the new CNN-LSTM network has the highest prediction accuracy (92%, 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.

Comparative Analysis of Convolutional Neural Network-Long Short-Term Memory, Sparrow Search Algorithm-Backpropagation Neural Network, and Particle Swarm Optimization-Extreme Learning Machine Models for the Water Discharge of the Buzău River, Romania

“…In such a way, improved performances of the combined model are obtained [47]. The new model can also extract nonlinear features and fluctuating trends [48].…”