Shale Crack Identification Based on Acoustic Emission Experiment and Wavenet Data Recovery

Abstract

This work provides a general analysis method for the crack detection in shales. The acoustic emission (AE) testing with data recovery is proposed for determining the crack modes and positions in the test and analysis process of the shale fracturing experiment. A fracturing and in situ AE monitoring system is constructed to collect the experimental data in at least six channels for the crack detection, and the source positions output from AE testing represent the positions of cracks. Due to some uncontrollable reasons, such as the poor coupling between sensors and sample and the sudden failure of the sensors, some parts of experimental data are missing during the experiments. Therefore, a data recovery neural network algorithm based on wavenet model is introduced to reconstruct the missing parts of experimental data in the waveforms. Since the accuracy of data recovery is not satisfying based on the collected experimental data, the interpolation of experimental data is performed to refine the data which can obviously improve the accuracy of data recovery. After all the required experimental data have been recovered, the crack mode for each crack can be determined based on the moment tensor analysis. This analysis method can be extensively applied to the shale crack detection.