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A high-precision time–frequency analysis for thin hydrocarbon reservoir identification based on synchroextracting generalized S-transform |
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| Authors: | Ying Hu Hui Chen Hongyan Qian Xinyue Zhou Yuanjun Wang Bin Lyu |
| Institution: | 1. Geomathematics Key Laboratory of Sichuan Province, Chengdu University of Technology, Chengdu, 610059 China
Postdoctoral Station of Geophysics, Chengdu University of Technology, Chengdu, 610059 China ConocoPhillips School of Geology and Geophysics, University of Oklahoma, Norman, OK, 73019 USA;2. Geomathematics Key Laboratory of Sichuan Province, Chengdu University of Technology, Chengdu, 610059 China;3. ConocoPhillips School of Geology and Geophysics, University of Oklahoma, Norman, OK, 73019 USA |
| 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. |
| Keywords: | Signal processing Seismic Data processing Reservoir geophysics |