Interval attenuation estimation from prestack seismic data: A case study from the Arabian Peninsula

Alshangiti, Abdullah; Zhou, Hua‐wei

doi:10.1007/s11770-020-0835-5

Cited by 3 publications

(4 citation statements)

References 39 publications

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“…When robustly estimated using carefully processed seismic gathers, attenuation values can be used as a mapping tool in quantitative interpretation. In this study, we use a τ – p domain prestack Q inversion method for attenuation estimation, which was shown to produce superior results in carbonate environments than other methods (Alshangiti & Zhou, 2020). We use a workflow that eliminates the subjectivity in selecting the regression bandwidth by using the median of attenuation estimates based on several regression bandwidths.…”

Section: Discussionmentioning

confidence: 99%

“…We use the τ – p domain prestack Q inversion (PSQI) method (Reine et al., 2012b) for

Q_{\rm{e}}^{ - 1}

estimations. We use the median of

Q_{\rm{e}}^{ - 1}

estimations based on several regression bandwidths to improve the PSQI output (Alshangiti & Zhou, 2020). Here, we provide a brief description of the method and workflow.…”

Section: Attenuation Estimationmentioning

confidence: 99%

“…So far, only a few attenuation studies have been conducted on carbonate rocks (e.g. Adam et al., 2009; Alshangiti & Zhou, 2020; Bouchaala et al., 2016b, a; Sun et al., 2002).…”

Section: Introductionmentioning

confidence: 99%

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Estimation of seismic attenuation for reservoirs mapping and inverse Q‐filtering: An application on land seismic data

Alshangiti

Zhou

2023

Geophysical Prospecting

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Seismic attenuation values based on inverse Q-filtering are useful in enhancing seismic data resolution for quantitative interpretation. However, as field attenuation estimations are often contaminated by noise and overburden effects, an inverse Q-filter may reduce the signal-to-noise ratio. To help reservoir mapping with seismic resolution enhancement, we use well logs and surface seismic data to estimate the attenuation of a depth interval composed of several carbonate oil reservoirs onshore the Arabian Peninsula. From applying a prestack Q inversion on τ-p gathers from a large 3D seismic survey, the estimated effective attenuation values show spatially coherent patterns, likely corresponding to changes in the petrophysical and fluid properties. The patterns of the estimated effective attenuation values correlate considerably with the anticline geometry, with higher values on its crest than its flanks. The small percentage of the negative effective attenuation values indicates that the apparent attenuation and overburden effects are insignificant. At well locations, the effective attenuation values show a good correlation with the average porosity of the main reservoir, verifying that they are related to changes in petrophysical properties in the analysis interval. We propose an extended inverse Q-filter with two additional parameters to make it more robust and flexible and test it with synthetic and field datasets. We estimate a multiple-scattering attenuation value from well logs and use it in the proposed filter for the field dataset. The compensation for the multiple-scattering effects suppresses the overprints of the data components unrelated to the target interval, hence benefitting the quantitative data interpretation.

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Section: Discussionmentioning

confidence: 99%

“…We use the τ – p domain prestack Q inversion (PSQI) method (Reine et al., 2012b) for

Q_{\rm{e}}^{ - 1}

estimations. We use the median of

Q_{\rm{e}}^{ - 1}

estimations based on several regression bandwidths to improve the PSQI output (Alshangiti & Zhou, 2020). Here, we provide a brief description of the method and workflow.…”

Section: Attenuation Estimationmentioning

confidence: 99%

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Estimation of seismic attenuation for reservoirs mapping and inverse Q‐filtering: An application on land seismic data

Alshangiti

Zhou

2023

Geophysical Prospecting

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“…Frequency‐based methods are typically used to extract attenuation from exploration seismic data, among which the spectral ratio method is commonly adopted in practice due to its reliability and practicality (e.g., Alshangiti & Zhou, 2020; Bath, 1974; Lei & Morgan, 2016). Based on the spectral ratio method, Dasgupta and Clark (1998) proposed a Q ‐versus‐offset (QVO) method to extract attenuation from the pre‐stack common midpoint gather (CMP), because CMP data is more common in real cases as its lower cost and wide coverage range.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Seismic Attenuation and Gas Hydrate Concentration From Surface Seismic Data at Hydrate Ridge, Cascadia Margin

Liu,

Bao,

Geng

et al. 2024

JGR Solid Earth

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An improved understanding of the effects of gas hydrate presence on seismic attenuation is important for accurate hydrate characterization and quantification. Based on a rock‐physics model recently presented for gas hydrate‐bearing fine‐grained clay‐dominated sediments, here we establish an integrated workflow for surface seismic data from extracting seismic attenuation to estimating gas hydrate concentration (Ch) in the sediment. We apply this workflow to the high‐resolution seismic data acquired at southern Hydrate Ridge, offshore Oregon, to reveal the hydrate distribution and clarify the controlling factor of hydrate formation. We first present an adaptive‐bandwidth spectral ratio method to robustly measure attenuation. The attenuation measurements show that the presence of hydrate suppresses the attenuation of the host sediment. We then calculate Ch by applying the rock‐physics model to the attenuation measurements. The estimated Ch are mostly low (<5%) in Hydrate Ridge and agrees well with the in‐situ Ch measured from core‐ or well log‐based data. Our result also suggests that the lithology and stratigraphic structures together control the distribution of gas hydrate at Hydrate Ridge, where relatively high Ch is found in the region where a gas‐charged conduit exists and in an anticlinal structure overlying a strong bottom simulating reflection. Adjacent to the anticline, however, a low amount of gas hydrate appears present, possibly due to the gas migration blocked by the anticlinal structure or the lack of gas conduits. Our study offers an effective strategy for detecting and quantifying gas hydrate in fine‐grained clayey sediments through surface seismic data.

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An effective Q extraction method via deep learning

Li,

Yu,

2024

Journal of Geophysics and Engineering

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Quality factor (Q) is a parameter reflecting the physical properties of reservoirs. Accurate estimation of the quality factor plays an important role in improving the resolution of seismic data. Spectral ratio method is a widely-used traditional method based on the linear least squares fitting to extract the quality factor, but is sensitive to noise. This is the main reason preventing this method from being widely used. Some supervised deep learning methods are proposed to extract Q, in which the construction of training labels is a key link. The proposed method is based on the spectral ratio method to create training labels, avoiding errors in generating them. In contrast to the least squares method, this paper proposes to use a nonlinear regression algorithm based on a fully connected network to fit the spectral logarithmic ratio and frequency. Meanwhile, the empirical equation is applied to constrain prediction results. The proposed method can effectively overcome the influence of noise and improve the accuracy of prediction results. Tests on the synthesized data of vertical seismic profile and common middle profile show that the proposed method has better generalization ability than the spectral ratio method. Applying the method to the field vertical seismic profile data successfully extracts the quality factor, which can provide effective information for dividing stratigraphic layers.

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Interval attenuation estimation from prestack seismic data: A case study from the Arabian Peninsula

Cited by 3 publications

References 39 publications

Estimation of seismic attenuation for reservoirs mapping and inverse Q‐filtering: An application on land seismic data

Estimation of seismic attenuation for reservoirs mapping and inverse Q‐filtering: An application on land seismic data

Estimation of Seismic Attenuation and Gas Hydrate Concentration From Surface Seismic Data at Hydrate Ridge, Cascadia Margin

An effective Q extraction method via deep learning

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