A high‐precision time–frequency analysis for thin hydrocarbon reservoir identification based on synchroextracting generalized S‐transform

Hu, Ying; Chen, Hui; Qian, Hongyan; Zhou, Xinyue; Yuan-jun, Wang; Lyu, Bin

doi:10.1111/1365-2478.12888

Cited by 18 publications

(3 citation statements)

References 25 publications

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“…A prominent example of this is the analysis of seismic-acoustic signals [7][8][9][10]. This type of signal exhibits the aforementioned characteristics and has been extensively investigated using time-frequency analysis [11][12][13]. The study of these acoustic signals is of great interest due to the valuable information they provide about geological structures [7,14,15].…”

Section: Introductionmentioning

confidence: 99%

Feature Extraction of a Non-Stationary Seismic–Acoustic Signal Using a High-Resolution Dyadic Spectrogram

Seuret-Jiménez,

Trutié-Carrero,

Nieto-Jalil

et al. 2023

Sensors

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Using a novel mathematical tool called the Te-gram, researchers analyzed the energy distribution of frequency components in the scale–frequency plane. Through this analysis, a frequency band of approximately 12 Hz is identified, which can be isolated without distorting its constituent frequencies. This band, along with others, remained inseparable through conventional time–frequency analysis methods. The Te-gram successfully addresses this knowledge gap, providing multi-sensitivity in the frequency domain and effectively attenuating cross-term energy. The Daubechies 45 wavelet function was employed due to its exceptional 150 dB attenuation in the rejection band. The validation process encompassed three stages: pre-, during-, and post-seismic activity. The utilized signal corresponds to the 19 September 2017 earthquake, occurring between the states of Morelos and Puebla, Mexico. The results showcased the impressive ability of the Te-gram to surpass expectations in terms of sensitivity and energy distribution within the frequency domain. The Te-gram outperformed the procedures documented in the existing literature. On the other hand, the results show a frequency band between 0.7 Hz and 1.75 Hz, which is named the planet Earth noise.

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Section: Introductionmentioning

confidence: 99%

Feature Extraction of a Non-Stationary Seismic–Acoustic Signal Using a High-Resolution Dyadic Spectrogram

Seuret-Jiménez,

Trutié-Carrero,

Nieto-Jalil

et al. 2023

Sensors

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“…However, the general ST has no parameters that can be used to change the Gaussian window function, which cannot be changed once it is selected. Therefore, many scholars [15][16] [17] have proposed a generalized S-transform (GST) with different window functions, which is widely used in non-stationary signal processing [18][19], such as feature extraction of seismic signals [20][21], reservoir identification [22][23], and seismic signal compensation [24][25]. To further optimize the TF resolution and energy aggregation of the ST, a new variable four-parameter Gaussian window function is proposed.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Generalized S-Transform Algorithm for Seismic Signal Analysis

et al. 2022

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As a powerful method in signal processing, time-frequency analysis shows the characteristics of signals in the form of a joint domain distribution of time and frequency. The S-transform is one of the most effective and frequently used algorithms for time-frequency analysis of seismic signals. To address the problems of fixed time window, such as single time-frequency resolution and poor energy aggregation, an adaptive generalized S-transform algorithm is proposed. In this method, we designed a new generalized Gaussian window function with four variable parameters to obtain the optimal time-frequency distribution. The proposed window function uses time-frequency aggregation as the objective function for parameter optimization. Combined with the calculation results of the time-frequency aggregation and time-frequency distribution, we proved that the proposed method has a satisfactory analysis effect and anti-noise performance. In addition, a simulation experiment of the seismic signal is carried out using this algorithm, and the results show that the proposed adaptive generalized S-transform enhances the time-frequency resolution and energy aggregation in seismic signal analysis.

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“…During this process, the seismic data are transformed from the time domain to the time-frequency (TF) domain (Chopra and Marfurt, 2015). Spectral decomposition has been used to determine layer thicknesses (Partyka et al, 1999), stratigraphic geometries (Marfurt and Kirlin, 2001;Puryear and Castagna, 2008) and direct hydrocarbon detection (Goloshubin et al, 2002;Castagna et al, 2003;Bao-li et al, 2011;Chen et al, 2020;Hu et al, 2020). TF transform is one of the critical parts of the spectral decomposition.…”

Section: Introductionmentioning

confidence: 99%

Application of adaptive parameterized S‐transform to delta sandstone reservoir identification

Fang

Chen

et al. 2021

Geophysical Prospecting

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Time-frequency analysis plays an important role in seismic interpretation and reservoir identification because it reveals the variation of seismic frequency contents over time. The S-transform is recognized as an efficient method for seismic time-frequency analysis, but its frequency distribution biases the actual Fourier spectrum due to the linear frequency-dependent term in the analytical window. In this paper, an improved S-transform, named adaptive parameterized S-transform, is proposed for identifying delta sandstone reservoirs. In the proposed method, three parameters are introduced into the S-transform to better adjust the time-frequency resolution in different frequency bands. Then these parameters are selected adaptively by using the concentration measure to provide a highly energy-concentrated time-frequency representation for seismic signals. Two synthetic examples validate the effectiveness of the proposed method. Analysis of field seismic data illustrates that it can provide a better reservoir interpretation with a high resolution. Comparisons between the wavelet transform, S-transform, and modified S-transform prove the superiority of the proposed method in describing reservoir distribution and decreasing the uncertainty of reservoir detection. This paper presents a complementary approach to current methods for reservoir detection and identification.

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A high‐precision time–frequency analysis for thin hydrocarbon reservoir identification based on synchroextracting generalized S‐transform

Cited by 18 publications

References 25 publications

Feature Extraction of a Non-Stationary Seismic–Acoustic Signal Using a High-Resolution Dyadic Spectrogram

Feature Extraction of a Non-Stationary Seismic–Acoustic Signal Using a High-Resolution Dyadic Spectrogram

An Adaptive Generalized S-Transform Algorithm for Seismic Signal Analysis

Application of adaptive parameterized S‐transform to delta sandstone reservoir identification

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