Synthetic shear sonic log generation utilizing hybrid machine learning techniques

Abstract

Compressional and shear sonic logs (DTC and DTS, respectively) are one of the effective means for determining petrophysical/geomechanical properties. However, the DTS log has limited availability mainly due to high acquisition costs. This study introduces a hybrid machine learning approach to generating synthetic DTS logs. Five wireline logs such as gamma ray (GR), density (RHOB), neutron porosity (NPHI), deep resistivity (Rt), and DTS logs are used as input data for three supervised-machine learning models including support vector machine for regression (SVR), deep neural network (DNN), and long short-term memory (LSTM). The hybrid machine learning model utilizes two additional techniques. First, as an unsupervised-learning approach, data clustering is integrated with general machine learning models for the purpose of improving model accuracy. All the machine learning models using the data-clustered approach show higher accuracies in predicting target (DTS) values, compared to non-clustered models. Second, particle swarm optimization (PSO) is combined with the models to determine optimal hyperparameters. The PSO algorithm proves time-effective, automated advantages as it gets feedback from previous computations so that is able to narrow down candidates for optimal hyperparameters. Compared to previous studies focusing on the performance comparison among machine learning algorithms, this study introduces an advanced approach to further improve the performance by integrating the unsupervised learning technique and PSO optimization with the general models. Based on this study result, we recommend the hybrid machine learning approach for improving the reliability and efficiency of synthetic log generation.