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

文献类型：期刊论文

作者Zheng, Yikang1,2; Wang, Yibo1,2; Liang, Xing3; Xue, Qingfeng1,2; Liang, Enmao4; Wu, Shaojiang1,2; An, Shujie5; Yao, Yi1,2; Liu, Chen3; Mei, Jue3
刊名FRONTIERS IN EARTH SCIENCE
出版日期2022-09-30
卷号10页码:9
关键词distributed acoustic sensing deep learning signal identification convolutional neural networks bidirectional long short-term memory
DOI10.3389/feart.2022.999530
英文摘要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.
资助项目CAS Project for Young Scientists in Basic Research ; National Natural Science Foundation of China[YSBR-020] ; [42025403]
WOS研究方向Geology
语种英语
WOS记录号WOS:000869479000001
出版者FRONTIERS MEDIA SA
资助机构CAS Project for Young Scientists in Basic Research ; CAS Project for Young Scientists in Basic Research ; CAS Project for Young Scientists in Basic Research ; CAS Project for Young Scientists in Basic Research ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; CAS Project for Young Scientists in Basic Research ; CAS Project for Young Scientists in Basic Research ; CAS Project for Young Scientists in Basic Research ; CAS Project for Young Scientists in Basic Research ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; CAS Project for Young Scientists in Basic Research ; CAS Project for Young Scientists in Basic Research ; CAS Project for Young Scientists in Basic Research ; CAS Project for Young Scientists in Basic Research ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; CAS Project for Young Scientists in Basic Research ; CAS Project for Young Scientists in Basic Research ; CAS Project for Young Scientists in Basic Research ; CAS Project for Young Scientists in Basic Research ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China
源URL[http://ir.iggcas.ac.cn/handle/132A11/107781]  
专题地质与地球物理研究所_兰州油气中心
通讯作者Wang, Yibo
作者单位1.Chinese Acad Sci, Inst Geol & Geophys, Key Lab Petr Resource Res, Beijing, Peoples R China
2.Chinese Acad Sci, Innovat Acad Earth Sci, Beijing, Peoples R China
3.PetroChina Zhejiang Oilfield Co, Hangzhou, Peoples R China
4.China State Shipbldg Corp Ltd, Res Inst 715, Hangzhou, Peoples R China
5.Opt Sci & Technol Chengdu Ltd, Chengdu, Peoples R China
推荐引用方式
GB/T 7714		 Zheng, Yikang,Wang, Yibo,Liang, Xing,et al. A deep learning approach for signal identification in the fluid injection process during hydraulic fracturing using distributed acoustic sensing data[J]. FRONTIERS IN EARTH SCIENCE,2022,10:9.
APA Zheng, Yikang.,Wang, Yibo.,Liang, Xing.,Xue, Qingfeng.,Liang, Enmao.,...&Mei, Jue.(2022).A deep learning approach for signal identification in the fluid injection process during hydraulic fracturing using distributed acoustic sensing data.FRONTIERS IN EARTH SCIENCE,10,9.
MLA Zheng, Yikang,et al."A deep learning approach for signal identification in the fluid injection process during hydraulic fracturing using distributed acoustic sensing data".FRONTIERS IN EARTH SCIENCE 10(2022):9.

入库方式： OAI收割

来源：地质与地球物理研究所

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