Examples of wavelet transform time‐frequency analysis in direct hydrocarbon detection

Sun, Shengjie; Castagna, John P.; Siegfried, Robert W.

doi:10.1190/1.1817281

Cited by 29 publications

(12 citation statements)

References 2 publications

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“…Comparison of different SFS sections can be utilized to detect low frequency shadows caused by hydrocarbon reservoirs. This method can potentially be utilized for direct hydrocarbon detection (Sun, Castagna and Siegfried, 2002). A seismic section from Ukpokiti, Nigeria shown in Figure 3 is interpreted using this method.…”

Section: A Single Frequency Seismic (Sfs) Sectionmentioning

confidence: 99%

Time‐frequency attribute of seismic data using continuous wavelet transform

Sinha

Routh

Anno

et al. 2003

SEG Technical Program Expanded Abstracts 2003

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A time-frequency decomposition that can provide higher frequency resolution at lower frequencies and higher time resolution at higher frequencies is desirable for analyzing seismic data. This is because the hydrocarbons in the reservoir are diagnostic at lower frequencies and thin beds can be resolved with enhanced time resolution at higher frequencies. In this paper we present a new method to compute the time-frequency spectrum using wavelet as a window that achieves this objective. Time-frequency spectrum is commonly used to compute various frequency attributes of seismic signal like single frequency, dominant frequency, center frequency and so forth. The conventional approach is to use short time Fourier transform (STFT) to obtain a time-frequency spectrum. Time-frequency resolution in the STFT is limited by the choice of a window length. The proposed time-frequency spectrum using CWT (TFCWT) in this work has the ability to adapt with the frequency content of the signal. The flexibility of not having to choose a window is an advantage of our method. We present two applications of TFCWT to real data sets in this paper. In the first example, we use TFCWT to enhance low frequency shadows caused by hydrocarbon reservoirs. In the second example, we apply the time frequency spectrum in interpreting time slices from a 3D seismic volume in frequency space to identify thin beds below tuning thickness.

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Section: A Single Frequency Seismic (Sfs) Sectionmentioning

confidence: 99%

Time‐frequency attribute of seismic data using continuous wavelet transform

Sinha

Routh

Anno

et al. 2003

SEG Technical Program Expanded Abstracts 2003

Self Cite

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“…It can be used to identify subtle thickness variations and discontinuities, as well as to predict bedding thicknesses (e.g., Partyka et al, 1999). Spectral decomposition can also be used to identify low-frequency shadow, which may indicate the presence of hydrocarbons (Sun et al, 2002;Wang, 2007) or as in this study, to identify good quality reservoirs upon calibration by the well-log porosity response.…”

Section: Introductionmentioning

confidence: 97%

Multiattribute framework analysis for the identification of carbonate mounds in the Brazilian presalt zone

et al. 2019

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Carbonate mounds, as described herein, often present seismic characteristics such as low amplitude and a high density of faults and fractures, which can easily be oversampled and blur other rock features in simple geobody extraction processes. We have developed a workflow for combining geometric attributes and hybrid spectral decomposition (HSD) to efficiently identify good-quality reservoirs in carbonate mounds within the complex environment of the Brazilian presalt zone. To better identify these reservoirs within the seismic volume of carbonate mounds, we divide our methodology into four stages: seismic data acquisition and processing overview, preconditioning of seismic data using structural-oriented filtering and imaging enhancement, calculation of seismic attributes, and classification of seismic facies. Although coherence and curvature attributes are often used to identify high-density fault and fracture zones, representing one of the most important features of carbonate mounds, HSD is necessary to discriminate low-amplitude carbonate mounds (good reservoir quality) from low-amplitude clay zones (nonreservoir). Finally, we use a multiattribute facies classification to generate a geologically significant outcome and to guide a final geobody extraction that is calibrated by well data and that can be used as a spatial indicator of the distribution of good reservoir quality for static modeling.

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“…The term "shadow" refers to a lowering of seismic frequencies from reflectors immediately beneath a reservoir horizon. For example, Sun et al (2002) reported that for a fractured carbonate reservoir, the spectral amplitude below the reservoir formation is severely attenuated on the 60-Hz instantaneous spectral analysis (ISA) component relative to the 40-Hz panel. The reason behind this low-frequency anomaly was explained as high-frequency energy attenuation caused by the gas reservoir, suggesting that low-frequency shadow can be used as a direct hydrocarbon indicator.…”

Section: Introductionmentioning

confidence: 98%

“…While using spectral decomposition for carbonate reservoir characterization, a number of cases have been reported that carbonate gas reservoirs exhibited so-called low-frequency shadows (Sun et al, 2002;Wang, 2007). The term "shadow" refers to a lowering of seismic frequencies from reflectors immediately beneath a reservoir horizon.…”

Section: Introductionmentioning

confidence: 99%

High-frequency anomalies in carbonate reservoir characterization using spectral decomposition

Zheng

Yan

2011

GEOPHYSICS

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Low-frequency shadows have often been used as hydrocarbon indicators in the application of spectral decomposition. The reason behind the low-frequency anomaly has been explained as high-frequency energy attenuation caused by hydrocarbons. However, in our practice on carbonate reservoir characterization in two areas, Precaspian Basin and Central Tarim Basin, China, we encountered high-frequency anomalies, i.e., the isofrequency slices or sections at high frequencies exhibit anomalies associated with the good carbonate reservoir, particularly in the tight limestone background. We used the product of porosity and thickness as a parameter to measure the quality of the carbonate reservoir of each well and classified the 46 wells in our two studied areas into three types. Type I wells contain high-porosity thick reservoirs, type II wells contain reservoirs with moderate porosity and thickness, and type III wells contain only low-porosity thin reservoirs. The results were that 12 out of 13 type I wells exhibit high-frequency anomalies, and 30 out of 33 type II and type III wells do not exhibit high-frequency anomalies. We further validated the existence of this high-frequency anomaly by forward modeling analysis and fluid substitution experiments using the actual well-log curves measured in the carbonate reservoir. The results showed that in our two studied areas the high-frequency anomalies are more common than low-frequency shadows that can be observed only when the thickness of the reservoir is more than half of the wavelength or the reservoir rocks are extremely unconsolidated. Therefore, this high-frequency anomaly may be used as a more reliable indicator for a good carbonate reservoir than low-frequency shadows in real applications.

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Examples of wavelet transform time‐frequency analysis in direct hydrocarbon detection

Cited by 29 publications

References 2 publications

Time‐frequency attribute of seismic data using continuous wavelet transform

Time‐frequency attribute of seismic data using continuous wavelet transform

Multiattribute framework analysis for the identification of carbonate mounds in the Brazilian presalt zone

High-frequency anomalies in carbonate reservoir characterization using spectral decomposition

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