A deep learning approach for signal identification in the fluid injection process during hydraulic fracturing using distributed acoustic sensing data

Abstract

Full-cycle and real-time monitoring of the wellbore flow during hydraulic fracturing is challenging in unconventional oil and gas development. In the past few years, distributed acoustic sensing (DAS) provides opportunities to measure the acoustic energy distribution along the entire horizontal well. It is a promising tool for real-time monitoring and understanding of the fluid injection process. However, the signal identification of effective flow in the wellbore from DAS data is cumbersome and prone to error. We propose a deep learning approach to solve this problem. The neural network is a combination of Convolutional Neural Networks (CNNs) and Bidirectional Long Short-Term Memory Networks (BiLSTM) to extract the spatial and temporal features from the DAS data. The trained model is applied to the field data collected in the horizontal well. The results demonstrate its capability for intelligent monitoring and real-time evaluation for hydraulic fracturing.