Next Article in Journal
Ultrasonic Phased Array Sparse-TFM Imaging Based on Sparse Array Optimization and New Edge-Directed Interpolation
Next Article in Special Issue
Online Model Updating and Dynamic Learning Rate-Based Robust Object Tracking
Previous Article in Journal
Improvement of Information Transfer Rates Using a Hybrid EEG-NIRS Brain-Computer Interface with a Short Trial Length: Offline and Pseudo-Online Analyses
Previous Article in Special Issue
Agreement Technologies for Energy Optimization at Home
 
 
Article

An Efficient Neural-Network-Based Microseismic Monitoring Platform for Hydraulic Fracture on an Edge Computing Architecture

by 1,2, 1,2, 1,2, 1,2,3,*, 3 and 3
1
College of Instrumentation and Electrical Engineering, Jilin University, Changchun 130061, China
2
Key Laboratory of Geophysical Exploration Equipment, Ministry of Education, Jilin University, Changchun 130061, China
3
School of Electrical and Data Engineering, University of Technology Sydney, Sydney, NSW 2007, Australia
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(6), 1828; https://doi.org/10.3390/s18061828
Received: 3 May 2018 / Revised: 2 June 2018 / Accepted: 3 June 2018 / Published: 5 June 2018
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Sensors Networks)
Microseismic monitoring is one of the most critical technologies for hydraulic fracturing in oil and gas production. To detect events in an accurate and efficient way, there are two major challenges. One challenge is how to achieve high accuracy due to a poor signal-to-noise ratio (SNR). The other one is concerned with real-time data transmission. Taking these challenges into consideration, an edge-computing-based platform, namely Edge-to-Center LearnReduce, is presented in this work. The platform consists of a data center with many edge components. At the data center, a neural network model combined with convolutional neural network (CNN) and long short-term memory (LSTM) is designed and this model is trained by using previously obtained data. Once the model is fully trained, it is sent to edge components for events detection and data reduction. At each edge component, a probabilistic inference is added to the neural network model to improve its accuracy. Finally, the reduced data is delivered to the data center. Based on experiment results, a high detection accuracy (over 96%) with less transmitted data (about 90%) was achieved by using the proposed approach on a microseismic monitoring system. These results show that the platform can simultaneously improve the accuracy and efficiency of microseismic monitoring. View Full-Text
Keywords: microseismic monitoring; event detection; edge computing; neural networks; probabilistic inference microseismic monitoring; event detection; edge computing; neural networks; probabilistic inference
Show Figures

Figure 1

MDPI and ACS Style

Zhang, X.; Lin, J.; Chen, Z.; Sun, F.; Zhu, X.; Fang, G. An Efficient Neural-Network-Based Microseismic Monitoring Platform for Hydraulic Fracture on an Edge Computing Architecture. Sensors 2018, 18, 1828. https://doi.org/10.3390/s18061828

AMA Style

Zhang X, Lin J, Chen Z, Sun F, Zhu X, Fang G. An Efficient Neural-Network-Based Microseismic Monitoring Platform for Hydraulic Fracture on an Edge Computing Architecture. Sensors. 2018; 18(6):1828. https://doi.org/10.3390/s18061828

Chicago/Turabian Style

Zhang, Xiaopu, Jun Lin, Zubin Chen, Feng Sun, Xi Zhu, and Gengfa Fang. 2018. "An Efficient Neural-Network-Based Microseismic Monitoring Platform for Hydraulic Fracture on an Edge Computing Architecture" Sensors 18, no. 6: 1828. https://doi.org/10.3390/s18061828

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop