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Abstract
Fracturing surface micro-seismic monitoring technology is widely used in fracturing surface. Micro- seismic monitoring conducted by means of the internet is composed of a large number of geophones laid on the ground to collect subsurface micro seismic signals and monitor fracturing fractures. With the advantages of a large amount of monitoring data, flexible layout, easy adjustment, and low cost. The technology also has some disadvantages, such as weak signal reception, vulnerable to environmental impact and low signal-to-noise ratio. In order to improve the positioning accuracy and receive as many effective signals as possible, a signal receiving network model of optimal geophone for micro-seismic monitoring on a fractured surface based on improved genetic algorithm is proposed. Through simulation and numerical analysis, the solution model has been optimized to meet the accuracy and quickly solve the optimal geophone array scheme. The results show that the optimal geophone array scheme not only satisfies the positioning accuracy but also achieves better reception of micro-seismic signals as possible with a small number of geophones, which reduces the cost and promotes the industrial application of this technology.
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Acknowledgments
This work was supported by Applied Basic Research Program of Sichuan Province, China(No.2016JY0049). The authors gratefully acknowledge the helpful comments and suggestions of the reviewers, which have improved the presentation.
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School of Mechatronic Engineering, Southwest Petroleum University, Chengdu, 610500, China
Haibo Liang, Xin Qin & Muhammad Junaid Khan
China National Offshore Oil Corporation Tianjin Branch, Tianjin, China
Jianchong Gao
- Haibo Liang
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Correspondence toHaibo Liang.
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This article is part of the Topical Collection:Special Issue on Future Networking Applications Plethora for Smart Cities
Guest Editors: Mohamed Elhoseny, Xiaohui Yuan, and Saru Kumari
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Liang, H., Qin, X., Gao, J.et al. Research on detector signal receiving network layout optimization model.Peer-to-Peer Netw. Appl.13, 1284–1296 (2020). https://doi.org/10.1007/s12083-019-00867-4
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