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Demodulation of EM Telemetry Data Using Fuzzy Wavelet Neural Network with Logistic Response

文献类型：期刊论文

作者Fayemi, Olalekan1,2,3,4; Di, Qingyun1,2,3,4; Zhen, Qihui1,2,3,4; Liang, Pengfei1,2,3,4
刊名APPLIED SCIENCES-BASEL
出版日期2021-11-01
卷号11期号:22页码:21
关键词demodulation EM telemetry fuzzy wavelet neural network logistic response
DOI10.3390/app112210877
英文摘要Data telemetry is a critical element of successful unconventional well drilling operations, involving the transmission of information about the well-surrounding geology to the surface in real-time to serve as the basis for geosteering and well planning. However, the data extraction and code recovery (demodulation) process can be a complicated system due to the non-linear and time-varying characteristics of high amplitude surface noise. In this work, a novel model fuzzy wavelet neural network (FWNN) that combines the advantages of the sigmoidal logistic function, fuzzy logic, a neural network, and wavelet transform was established for the prediction of the transmitted signal code from borehole to surface with effluent quality. Moreover, the complete workflow involved the pre-processing of the dataset via an adaptive processing technique before training the network and a logistic response algorithm for acquiring the optimal parameters for the prediction of signal codes. A data reduction and subtractive scheme are employed as a pre-processing technique to better characterize the signals as eight attributes and, ultimately, reduce the computation cost. Furthermore, the frequency-time characteristics of the predicted signal are controlled by selecting an appropriate number of wavelet bases "N " and the pre-selected range for pij3 to be used prior to the training of the FWNN system. The results, leading to the prediction of the BPSK characteristics, indicate that the pre-selection of the N value and pij3 range provides a significantly accurate prediction. We validate its prediction on both synthetic and pseudo-synthetic datasets. The results indicated that the fuzzy wavelet neural network with logistic response had a high operation speed and good quality prediction, and the correspondingly trained model was more advantageous than the traditional backward propagation network in prediction accuracy. The proposed model can be used for analyzing signals with a signal-to-noise ratio lower than 1 dB effectively, which plays an important role in the electromagnetic telemetry system.
资助项目Strategic Priority Research Program of the Chinese Academy of Sciences[XDA140501000]
WOS研究方向Chemistry ; Engineering ; Materials Science ; Physics
语种英语
WOS记录号WOS:000727963700001
出版者MDPI
资助机构Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences
源URL[http://ir.iggcas.ac.cn/handle/132A11/103902]  
专题地质与地球物理研究所_深部资源勘探装备研发
地质与地球物理研究所_中国科学院页岩气与地质工程重点实验室
通讯作者Fayemi, Olalekan; Di, Qingyun
作者单位1.Chinese Acad Sci, Inst Earth Sci, Beijing 100029, Peoples R China
2.Chinese Acad Sci, Inst Geol & Geophys, CAS Engn Lab Deep Resources Equipment & Technol, Beijing 100029, Peoples R China
3.Chinese Acad Sci, Inst Geol & Geophys, Key Lab Shale Gas & Geoengn, Beijing 100029, Peoples R China
4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714		 Fayemi, Olalekan,Di, Qingyun,Zhen, Qihui,et al. Demodulation of EM Telemetry Data Using Fuzzy Wavelet Neural Network with Logistic Response[J]. APPLIED SCIENCES-BASEL,2021,11(22):21.
APA Fayemi, Olalekan,Di, Qingyun,Zhen, Qihui,&Liang, Pengfei.(2021).Demodulation of EM Telemetry Data Using Fuzzy Wavelet Neural Network with Logistic Response.APPLIED SCIENCES-BASEL,11(22),21.
MLA Fayemi, Olalekan,et al."Demodulation of EM Telemetry Data Using Fuzzy Wavelet Neural Network with Logistic Response".APPLIED SCIENCES-BASEL 11.22(2021):21.

入库方式： OAI收割

来源：地质与地球物理研究所

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