Gas prediction using low-frequency components of variable-depth streamer seismic data applied to the deepwater area of the South China Sea

Wang, Yanchao; Handong, Huang; Yuan, Sanyi; Zhang, Sheng; Li, Bowen

doi:10.1016/j.jngse.2016.07.071

Cited by 9 publications

(4 citation statements)

References 15 publications

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“…where κ is permeability and η is fluid viscosity; i is the imaginary unit; ρ b is rock bulk density; R 0 and R 1 are real and frequency-independent terms related to porosity and elastic properties of fluids and solid matrix. Chen et al [32] and Wang et al [33] used the factor FM = κ/η as an indicator of fluid mobility and then expressed equation ( 14) as…”

Section: Methodsmentioning

confidence: 99%

“…Equation ( 17) shows that FM is proportional to the squared derivative of the reflection coefficient to the frequency at a specific frequency. Chen et al [32] and Wang et al [33] suggested representing the magnitude of the reflection coefficient at a particular frequency f using decomposed spectrum Sð f Þ. Thus, fluid mobility attribute FM can be estimated by…”

Section: Methodsmentioning

confidence: 99%

“…Chen [32] proved that fluid mobility could be positively correlated with the derivative of the reflection coefficient for the frequency and suggested using the instantaneous spectrum of seismic data in applications. At the same time, Wang et al [33] and Luo et al [34] predicted gas reservoirs using the fluid mobility derived from the low-frequency component of the marine seismic data.…”

Section: Introductionmentioning

confidence: 99%

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Characterization of a Volcanic Gas Reservoir Using Seismic Dispersion and Fluid Mobility Attributes

Guo

Liu

et al. 2021

Lithosphere

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Seismic dispersion and fluid mobility attributes are used to characterize a volcanic gas reservoir in the Songliao Basin of China. A rock physics model is constructed to describe poroelastic behaviors associated with heterogeneous fluids saturation within the volcanic gas reservoirs, where velocity dispersion and attenuation of propagating waves are attributed to the wave-induced fluid flow described by the patchy saturation theory. Modeling results indicate that the frequency-dependent bulk modulus at the seismic frequency is more sensitive to gas saturation than the P-wave velocity dispersion. Accordingly, a new inversion method is developed to compute bulk-modulus-related dispersion attribute DK for improved characterization of volcanic gas reservoirs. Synthetic tests indicate that DK is more sensitive than traditional P-wave dispersion attribute DP to the variations of reservoir properties. The high value of dispersion attribute DK indicates the volcanic gas reservoirs with high porosity and gas saturation. At the same time, fluid mobility attribute FM can discriminate the volcanic gas reservoir as DK. Field data applications illustrate that DK and FM exhibit anomalies to the gas zones in the volcanic gas reservoir on the cross-well section. However, DK is more robust than FM to identify favorable zones on horizontal slices for specific target layers. Overall, rock physical modeling provides insights into the poroelastic behaviors of volcanic gas reservoirs, and inversion for seismic dispersion attribute DK improves hydrocarbon detection in the volcanic gas reservoir.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Characterization of a Volcanic Gas Reservoir Using Seismic Dispersion and Fluid Mobility Attributes

Guo

Liu

et al. 2021

Lithosphere

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“…Seismic attenuation is an important indicator for the identification of gasbearing hydrocarbons. The conventional method in gas-bearing prediction is to extract information on gas-sensitive seismic attributes from seismic data (Wang et al, 2020), for example, seismic attenuation attributes (Wang et al, 2016). Therefore, methods such as Fourier transform, wavelet transform, S transform, and synchronous compression wavelet transform (SCWT) are commonly used to conduct time-frequency analysis when locating gas in abnormal positions (Fawad et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Hydrocarbon detection based on empirical mode decomposition, teager-kaiser energy, and the cepstrum

Jiang

Cao

et al. 2022

Front. Earth Sci.

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Seismic waves produce anomalies when they pass through hydrocarbons; these anomalies, which are commonly used to detect hydrocarbons, are manifested differently in different domains. Here, we propose a novel hydrocarbon detection method that combines Empirical Mode Decomposition (EMD), the Teager-Kaiser energy operator (TKEO), and the cepstrum. This method utilizes EMD’s ability to adaptively decompose signals, benefits from the TKEO’s superior performance regarding the focusing of instantaneous energy, and uses the sensitivity of cepstrum domain parameters to hydrocarbons. Here, applying the developed EMD-TKE-Cepstrum method to the Marmousi2 example revealed that it could describe the position and extent of hydrocarbons more clearly than the synchronous compression wavelet transform (SCWT) method. Applying the EMD-TKE-Cepstrum algorithm to field data further confirmed its potential regarding the identification of anomalies associated with hydrocarbon reservoirs.

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Gas Reservoir Characterization Using Lp-Norm Constrained High-Resolution Seismic Spectral Attributes

Wang

Yuan

Wang

et al. 2020

Pure Appl. Geophys.

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Gas prediction using low-frequency components of variable-depth streamer seismic data applied to the deepwater area of the South China Sea

Cited by 9 publications

References 15 publications

Characterization of a Volcanic Gas Reservoir Using Seismic Dispersion and Fluid Mobility Attributes

Characterization of a Volcanic Gas Reservoir Using Seismic Dispersion and Fluid Mobility Attributes

Hydrocarbon detection based on empirical mode decomposition, teager-kaiser energy, and the cepstrum

Gas Reservoir Characterization Using Lp-Norm Constrained High-Resolution Seismic Spectral Attributes

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