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Demodulation of EM Telemetry Data Using Fuzzy Wavelet Neural Network with Logistic Response
Fayemi, Olalekan1,2,3,4; Di, Qingyun1,2,3,4; Zhen, Qihui1,2,3,4; Liang, Pengfei1,2,3,4
2021-11-01
Source PublicationAPPLIED SCIENCES-BASEL
Volume11Issue:22Pages:21
AbstractData 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.
Keyworddemodulation EM telemetry fuzzy wavelet neural network logistic response
DOI10.3390/app112210877
Funding OrganizationStrategic 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
Language英语
Funding ProjectStrategic Priority Research Program of the Chinese Academy of Sciences[XDA140501000]
Funding OrganizationStrategic 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
WOS Research AreaChemistry ; Engineering ; Materials Science ; Physics
WOS SubjectChemistry, Multidisciplinary ; Engineering, Multidisciplinary ; Materials Science, Multidisciplinary ; Physics, Applied
WOS IDWOS:000727963700001
PublisherMDPI
Citation statistics
Document Type期刊论文
Identifierhttp://ir.iggcas.ac.cn/handle/132A11/103902
Collection深部资源探测先导技术与装备研发中心
页岩气与地质工程院重点实验室
Corresponding AuthorFayemi, Olalekan; Di, Qingyun
Affiliation1.Chinese Acad Sci, Inst Geol & Geophys, CAS Engn Lab Deep Resources Equipment & Technol, Beijing 100029, Peoples R China
2.Chinese Acad Sci, Inst Geol & Geophys, Key Lab Shale Gas & Geoengn, Beijing 100029, Peoples R China
3.Chinese Acad Sci, Inst Earth Sci, Beijing 100029, Peoples R China
4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
First Author AffilicationInstitute of Geology and Geophysics, Chinese Academy of Sciences;  Key Laboratory of Shale Gas and Geoengineering, Institute of Geology and Geophysics, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Geology and Geophysics, Chinese Academy of Sciences;  Key Laboratory of Shale Gas and Geoengineering, Institute of Geology and Geophysics, Chinese Academy of Sciences
Recommended Citation
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.
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