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...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Olalekan Fayemi, Qingyun Di, Qihui Zhen, Pengfei Liang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	demodulation EM telemetry fuzzy wavelet neural network logistic response Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/7e53d774c1514bb3a247ccf883a633e7
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:7e53d774c1514bb3a247ccf883a633e7
record_format	dspace
spelling	oai:doaj.org-article:7e53d774c1514bb3a247ccf883a633e72021-11-25T16:39:42ZDemodulation of EM Telemetry Data Using Fuzzy Wavelet Neural Network with Logistic Response10.3390/app1122108772076-3417https://doaj.org/article/7e53d774c1514bb3a247ccf883a633e72021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/22/10877https://doaj.org/toc/2076-3417Data 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.Olalekan FayemiQingyun DiQihui ZhenPengfei LiangMDPI AGarticledemodulationEM telemetryfuzzy wavelet neural networklogistic responseTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10877, p 10877 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	demodulation EM telemetry fuzzy wavelet neural network logistic response Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	demodulation EM telemetry fuzzy wavelet neural network logistic response Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Olalekan Fayemi Qingyun Di Qihui Zhen Pengfei Liang Demodulation of EM Telemetry Data Using Fuzzy Wavelet Neural Network with Logistic Response
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.
format	article
author	Olalekan Fayemi Qingyun Di Qihui Zhen Pengfei Liang
author_facet	Olalekan Fayemi Qingyun Di Qihui Zhen Pengfei Liang
author_sort	Olalekan Fayemi
title	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_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_sort	demodulation of em telemetry data using fuzzy wavelet neural network with logistic response
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/7e53d774c1514bb3a247ccf883a633e7
work_keys_str_mv	AT olalekanfayemi demodulationofemtelemetrydatausingfuzzywaveletneuralnetworkwithlogisticresponse AT qingyundi demodulationofemtelemetrydatausingfuzzywaveletneuralnetworkwithlogisticresponse AT qihuizhen demodulationofemtelemetrydatausingfuzzywaveletneuralnetworkwithlogisticresponse AT pengfeiliang demodulationofemtelemetrydatausingfuzzywaveletneuralnetworkwithlogisticresponse
_version_	1718413086300307456

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

Ejemplares similares