Seismic attenuation attributes with applications on conventional and unconventional reservoirs

Li, Fangyu; Verma, Sumit; Zhou, Hao-Miao; Zhao, Tao; Marfurt, Kurt J.

doi:10.1190/int-2015-0105.1

Cited by 35 publications

(6 citation statements)

References 23 publications

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“…These apparent changes in the porosity are also controlled by the factors of hydrate pore filling and compaction [28] of sediments, which induce an attenuation effect as the seismic wave propagates through one medium into the other. Seismic attenuation is therefore a crucial tool in indicating pore structure, fluid content, and lithology [29,30], and therefore plays an important role in the approach to discerning BSRs.…”

Section: Seismic Attenuationmentioning

confidence: 99%

“…The quality factor, Q, measures the degree of dissipation of a material such that high Q values indicate low dissipation/attenuation and lower Q values indicate high dissipation/attenuation of signals. Seismic attenuation is therefore a crucial tool in indicating pore structure, fluid content, and lithology [29,30], and thus plays an important role in the approach to discerning BSR's. The quality factor will therefore be the attenuation parameter observed for the application of this study.…”

Section: Attenuation Application: Sparse-spike Decompositionmentioning

confidence: 99%

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Seismic Attribute Analyses and Attenuation Applications for Detecting Gas Hydrate Presence

et al. 2021

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Identifying gas hydrates in the oceanic subsurface using seismic reflection data supported by the presence of a bottom simulating reflector (BSR) is not an easy task, given the wide range of geophysical methods that have been applied to do so. Though the presence of the BSR is attributed to the attenuation response, as seismic waves transition from hydrate-filled sediment within the gas hydrate stability zone (GHSZ) to free gas-bearing sediment below, few studies have applied a direct attenuation measurement. To improve the detection of gas hydrates and associated features, including the BSR and free gas accumulation beneath the gas hydrates, we apply a recently developed method known as Sparse-Spike Decomposition (SSD) that directly measures attenuation from estimating the quality factor (Q) parameter. In addition to performing attribute analyses using frequency attributes and a spectral decomposition method to improve BSR imaging, using a comprehensive analysis of the three methods, we make several key observations. These include the following: (1) low-frequency shadow zones seem to correlate with large values of attenuation; (2) there is a strong relationship between the amplitude strength of the BSR and the increase of the attenuation response; (3) the resulting interpretation of migration pathways of the free gas using the direct attenuation measurement method; and (4) for the data analyzed, the gas hydrates themselves do not give rise to either impedance or attenuation anomalies that fully differentiate them from nearby non-hydrate zones. From this last observation, we find that, although the SSD method may not directly detect in situ gas hydrates, the same gas hydrates often form an effective seal trapping and deeper free gas accumulation, which can exhibit a large attenuation response, allowing us to infer the likely presence of the overlying hydrates themselves.

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Section: Seismic Attenuationmentioning

confidence: 99%

Section: Attenuation Application: Sparse-spike Decompositionmentioning

confidence: 99%

Seismic Attribute Analyses and Attenuation Applications for Detecting Gas Hydrate Presence

et al. 2021

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“…When seismic waves propagate through strata, fluid causes attenuation of seismic waves (Cadoret et al 1998;Yin et al 2015). Seismic attenuation shows that high-frequency components decay more rapidly than low-frequency components, and this attenuation characteristic has been widely exploited for hydrocarbon reservoir identification (Müller et al 2010;Xiong et al 2011;Li et al 2016b;Tary et al 2017). Thus, we use field data to demonstrate the superior performance of the SEGST by analysing energy of constant frequency sections.…”

Section: F I E L D D a T A E X A M P L Ementioning

confidence: 99%

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

Chen

Qian

et al. 2019

Geophysical Prospecting

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A B S T R A C TImproving 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 Stransform has great application prospects and merits in seismic signal processing and interpretation.

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“…Seismic signals are often disturbed by noise, and consequently, the signal-to-noise ratio (SNR) of seismic signals is typically low. A low SNR indicates poor signal quality, which affects downstream signal processing, such as deconvolution [1], seismic in-version [2], and seismic attribute analysis [3]. Therefore, seismic signals must be pre-processed to obtain clear and high-resolution seismic profiles [4,5], and effective noise removal methods are critical for seismic signal pre-processing [6,7].…”

Section: Introductionmentioning

confidence: 99%

Seismic Random Noise Attenuation Using a Tied-Weights Autoencoder Neural Network

Zhou

Guo

2021

Minerals

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Random noise is unavoidable in seismic data acquisition due to anthropogenic impacts or environmental influences. Therefore, random noise suppression is a fundamental procedure in seismic signal processing. Herein, a deep denoising convolutional autoencoder network based on self-supervised learning was developed herein to attenuate seismic random noise. Unlike conventional methods, our approach did not use synthetic clean data or denoising results as a training label to build the training and test sets. We directly used patches of raw noise data to establish the training set. Subsequently, we designed a robust deep convolutional neural network (CNN), which only depended on the input noise dataset to learn hidden features. The mean square error was then evaluated to establish the cost function. Additionally, tied weights were used to reduce the risk of over-fitting and improve the training speed to tune the network parameters. Finally, we denoised the target work area signals using the trained CNN network. The final denoising result was obtained after patch recombination and inverse operation. Results based on synthetic and real data indicated that the proposed method performs better than other novel denoising methods without loss of signal quality loss.

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Seismic attenuation attributes with applications on conventional and unconventional reservoirs

Cited by 35 publications

References 23 publications

Seismic Attribute Analyses and Attenuation Applications for Detecting Gas Hydrate Presence

Seismic Attribute Analyses and Attenuation Applications for Detecting Gas Hydrate Presence

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

Seismic Random Noise Attenuation Using a Tied-Weights Autoencoder Neural Network

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