Demodulation of EM Telemetry Data Using Fuzzy Wavelet Neural Network with Logistic Response
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...
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| Format: | Article |
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MDPI AG
2021-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/22/10877 |
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| author | Olalekan Fayemi Qingyun Di Qihui Zhen Pengfei Liang |
| author_facet | Olalekan Fayemi Qingyun Di Qihui Zhen Pengfei Liang |
| author_sort | Olalekan Fayemi |
| collection | DOAJ |
| description | 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mi>p</mi><mrow><mi>i</mi><mi>j</mi></mrow><mn>3</mn></msubsup></mrow></semantics></math></inline-formula> 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mi>p</mi><mrow><mi>i</mi><mi>j</mi></mrow><mn>3</mn></msubsup></mrow></semantics></math></inline-formula> 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. |
| first_indexed | 2024-03-10T05:43:17Z |
| format | Article |
| id | doaj.art-7e53d774c1514bb3a247ccf883a633e7 |
| institution | Directory Open Access Journal |
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| last_indexed | 2024-03-10T05:43:17Z |
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| spelling | doaj.art-7e53d774c1514bb3a247ccf883a633e72023-11-22T22:20:18ZengMDPI AGApplied Sciences2076-34172021-11-0111221087710.3390/app112210877Demodulation of EM Telemetry Data Using Fuzzy Wavelet Neural Network with Logistic ResponseOlalekan Fayemi0Qingyun Di1Qihui Zhen2Pengfei Liang3CAS Engineering Laboratory for Deep Resources Equipment and Technology, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, ChinaCAS Engineering Laboratory for Deep Resources Equipment and Technology, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, ChinaCAS Engineering Laboratory for Deep Resources Equipment and Technology, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, ChinaCAS Engineering Laboratory for Deep Resources Equipment and Technology, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, ChinaData 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mi>p</mi><mrow><mi>i</mi><mi>j</mi></mrow><mn>3</mn></msubsup></mrow></semantics></math></inline-formula> 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mi>p</mi><mrow><mi>i</mi><mi>j</mi></mrow><mn>3</mn></msubsup></mrow></semantics></math></inline-formula> 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.https://www.mdpi.com/2076-3417/11/22/10877demodulationEM telemetryfuzzy wavelet neural networklogistic response |
| spellingShingle | Olalekan Fayemi Qingyun Di Qihui Zhen Pengfei Liang Demodulation of EM Telemetry Data Using Fuzzy Wavelet Neural Network with Logistic Response Applied Sciences demodulation EM telemetry fuzzy wavelet neural network logistic response |
| title | Demodulation of EM Telemetry Data Using Fuzzy Wavelet Neural Network with Logistic Response |
| title_full | Demodulation of EM Telemetry Data Using Fuzzy Wavelet Neural Network with Logistic Response |
| title_fullStr | Demodulation of EM Telemetry Data Using Fuzzy Wavelet Neural Network with Logistic Response |
| title_full_unstemmed | Demodulation of EM Telemetry Data Using Fuzzy Wavelet Neural Network with Logistic Response |
| title_short | Demodulation of EM Telemetry Data Using Fuzzy Wavelet Neural Network with Logistic Response |
| title_sort | demodulation of em telemetry data using fuzzy wavelet neural network with logistic response |
| topic | demodulation EM telemetry fuzzy wavelet neural network logistic response |
| url | https://www.mdpi.com/2076-3417/11/22/10877 |
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