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A high‐precision time–frequency analysis for thin hydrocarbon reservoir identification based on synchroextracting generalized S‐transform
Author(s) -
Hu Ying,
Chen Hui,
Qian Hongyan,
Zhou Xinyue,
Wang Yuanjun,
Lyu Bin
Publication year - 2020
Publication title -
geophysical prospecting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.735
H-Index - 79
eISSN - 1365-2478
pISSN - 0016-8025
DOI - 10.1111/1365-2478.12888
Subject(s) - algorithm , time–frequency analysis , signal (programming language) , computer science , instantaneous phase , signal processing , s transform , noise (video) , geology , artificial intelligence , image (mathematics) , digital signal processing , wavelet transform , telecommunications , radar , wavelet packet decomposition , wavelet , computer hardware , programming language
ABSTRACT Improving the seismic time–frequency resolution is a crucial step for identifying thin reservoirs. In this paper, we propose a new high‐precision time–frequency analysis algorithm, synchroextracting generalized S‐transform, which exhibits superior performance at characterizing reservoirs and detecting hydrocarbons. This method first calculates time–frequency spectra using generalized S‐transform; then, it squeezes all but the most smeared time–frequency coefficients into the instantaneous frequency trajectory and finally obtains highly accurate and energy‐concentrated time–frequency spectra. We precisely deduce the mathematical formula of the synchroextracting generalized S‐transform. Synthetic signal examples testify that this method can correctly decompose a signal and provide a better time–frequency representation. The results of a synthetic seismic signal and real seismic data demonstrate that this method can identify some reservoirs with thincknesses smaller than a quarter wavelength and can be successfully applied for hydrocarbon detection. In addition, examples of synthetic signals with different levels of Gaussian white noise show that this method can achieve better results under noisy conditions. Hence, the synchroextracting generalized S‐transform has great application prospects and merits in seismic signal processing and interpretation.