High-Order Synchroextracting Time-Frequency Analysis and Its Application in Seismic Hydrocarbon Reservoir Identification

Time-frequency (TF) analysis (TFA) plays an important role in seismic hydrocarbon reservoir identification, which is attributed to its ability to effectively identify the oil and gas seismic response characteristics of geological bodies in different frequency bands. In this letter, we introduce a no...

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Veröffentlicht in:	IEEE geoscience and remote sensing letters Jg. 18; H. 11; S. 2011 - 2015
Hauptverfasser:	Chen, Xuping, Chen, Hui, Fang, Yuxia, Hu, Ying
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.11.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Fourier analysis Fourier transforms Frequencies Frequency analysis Frequency dependence Geology High-order synchroextracting Hydrocarbon reservoirs Hydrocarbons Identification Mathematical model Oil reservoirs Oils Reservoirs Seismic activity Seismic data seismic hydrocarbon reservoir identification Seismic response Seismological data synchroextracting operator (SEO) Time-frequency analysis time–frequency analysis (TFA) Transforms
ISSN:	1545-598X, 1558-0571
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Abstract	Time-frequency (TF) analysis (TFA) plays an important role in seismic hydrocarbon reservoir identification, which is attributed to its ability to effectively identify the oil and gas seismic response characteristics of geological bodies in different frequency bands. In this letter, we introduce a novel TFA method termed high-order synchroextracting transform (SET) and apply it to the high-precision identification of gas-bearing reservoirs. Under the premise of short-time Fourier transform (STFT), this method defines a new synchroextracting operator (SEO) based on high-order approximations of signal amplitude and phase. Furthermore, only the TF information highly correlated with the TF characteristics of the signal is extracted from the STFT spectrum by using the SEO. Therefore, for a wider variety of the nonstationary signal, a highly energy-concentrated TF representation can be effectively obtained. The application of STFT and different-order SET on 1-D synthetic signal and field seismic data verifies the effectiveness of the proposed method.
AbstractList	Time-frequency (TF) analysis (TFA) plays an important role in seismic hydrocarbon reservoir identification, which is attributed to its ability to effectively identify the oil and gas seismic response characteristics of geological bodies in different frequency bands. In this letter, we introduce a novel TFA method termed high-order synchroextracting transform (SET) and apply it to the high-precision identification of gas-bearing reservoirs. Under the premise of short-time Fourier transform (STFT), this method defines a new synchroextracting operator (SEO) based on high-order approximations of signal amplitude and phase. Furthermore, only the TF information highly correlated with the TF characteristics of the signal is extracted from the STFT spectrum by using the SEO. Therefore, for a wider variety of the nonstationary signal, a highly energy-concentrated TF representation can be effectively obtained. The application of STFT and different-order SET on 1-D synthetic signal and field seismic data verifies the effectiveness of the proposed method.
Author	Chen, Xuping Chen, Hui Fang, Yuxia Hu, Ying
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Snippet	Time-frequency (TF) analysis (TFA) plays an important role in seismic hydrocarbon reservoir identification, which is attributed to its ability to effectively... Time–frequency (TF) analysis (TFA) plays an important role in seismic hydrocarbon reservoir identification, which is attributed to its ability to effectively...
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SubjectTerms	Fourier analysis Fourier transforms Frequencies Frequency analysis Frequency dependence Geology High-order synchroextracting Hydrocarbon reservoirs Hydrocarbons Identification Mathematical model Oil reservoirs Oils Reservoirs Seismic activity Seismic data seismic hydrocarbon reservoir identification Seismic response Seismological data synchroextracting operator (SEO) Time-frequency analysis time–frequency analysis (TFA) Transforms
Title	High-Order Synchroextracting Time-Frequency Analysis and Its Application in Seismic Hydrocarbon Reservoir Identification
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