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1

Intelligent velocity picking and uncertainty analysis based on the Gaussian mixture model

Wang Xiaowei, Gao Yang, Chen Chang, Yuan Huan, Yuan Sanyi

Acta Geophysica

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2022

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Vol. 70, no 6

2659--2673

EN

The stacking velocity is often obtained manually. However, manually picking is inefficient and is easily affected by subjective factors such as the priori information and the experience of different processors. To enhance its objectivity, efficiency and consistency, we investigated an unsupervised clustering intelligent velocity picking method based on the Gaussian mixture model (GMM). This method can automatically pick the stacking velocity fast, and provide uncertainty analysis as a quality control. Combined with the geometry feature of energy clusters in velocity spectra, taking advantages of the geometric diversity of energy clusters, GMM can ft the energy clusters with different distributions more appropriately. Then, mean values of the final several submodels are located as the optimal velocity, and the multiples are avoided under the expert knowledge and geological rules. In addition, according to the covariance of submodels, we can derive the uncertainty analysis of the final time-velocity pairs, so as to indicate the reliability of picking velocity at different depths. Moreover, the automated interpreted velocity field is used for both normal moveout (NMO) correction and stacking. The comparison with the manual references is adopted to evaluate the quality of the unsupervised clustering intelligent velocity picking method. Both synthetic data and 3D field data have shown that the proposed unsupervised intelligent velocity picking method can not only achieve similar accuracy with manual results, but also get rid of multiples. Furthermore, compared with manual picking, it can significantly improve the efficiency and accuracy in identifying pore and cave structures, as well as indicating the uncertainty of time-velocity pairs by variance.

2

Multichannel sparse spike deconvolution based on dynamic time warping

Zhao Xiaowei, Wang Shangxu, Yuan Sanyi, Cheng Liang, Cai Youjun

Acta Geophysica

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2021

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Vol. 69, no. 3

783--793

EN

Seismic sparse spike deconvolution is commonly used to invert for subsurface refectivity series and is usually implemented as an inversion scheme. Conventional sparse spike deconvolution method does not utilize the relationships among adjacent traces resulting in instability and poor lateral continuity of the inverted result. We propose a multichannel sparse spike deconvolution method with a sparsity-promoting constraint and an extra lateral constraint exploiting the spatial relationships among adjacent seismic traces. Firstly, the dynamic time warping (DTW) is performed between any two adjoining seismic traces to obtain the warping path (a series of estimated time shifts of one seismic trace relative to the other). Based on the assumption that if the inverted refectivity series is convolved with the same wavelet used for inversion, the newly constructed adjoining seismic traces shall also be conformable to the relationships exploited among the original seismic traces by DTW. A diference operator is constructed with the estimated time shifts to guarantee the diference operation is performed between corresponding time samples on adjoining seismic traces and the inversion is regularized with this diference operator as the lateral constraint. Synthetic and real data case studies confrm that inverted result obtained by the proposed method is superior to those obtained by single-channel sparse spike deconvolution method and another multichannel deconvolution method based on horizontal frst-order derivative constraint in both signal-to-noise ratio and lateral continuity.

3

Low porosity and low permeability reservoirs characterization using low frequency seismic attribute

Zeng Jing, Huang Handong, Yuan Sanyi, Wu Chunhua

Acta Geophysica

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2020

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Vol. 68, no. 5

1345--1360

EN

The low-frequency seismic refection is important for the characterization of hydrocarbon reservoirs. Previous research has proved that the frequency-dependent component of the low-frequency refection coefcient is approximately proportional to the reservoir fuid mobility. A low-frequency seismic attribute is defned to extract an approximate measurement of reservoir fuid mobility from seismic refection data. Based on the high-resolution time–frequency decomposition technology, we apply this seismic attribute to detect hydrocarbons in shale and igneous reservoirs with low porosity and low permeability. The application results illustrate that the low-frequency seismic attribute can not only indicate the spatial distribution of oil and gas, which can help design the optimal landing point and trajectory of directional drilling, but also be approximately proportional to the production of oil and gas, which can be used for resource evaluation during the early exploration stage. We also give a brief discussion on the working mechanism of the low-frequency seismic attribute. The present work may provide some references for the subsequent exploration and research of reservoirs with low porosity and low permeability.

4

The influence of the frequency‑dependent spherical‑wave effect on the near‑surface attenuation estimation

Ji Yongzhen, Wang Shangxu, Yuan Sanyi, Yan Binpeng

Acta Geophysica

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2019

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Vol. 67, no. 2

577--587

EN

The knowledge of Q is desirable for improving seismic resolution, facilitating amplitude analysis and seismic interpretation. The most commonly used methods for Q estimation are the frequency-spectrum-based methods. Generally, these methods are based on the plane wave theory assuming that the transmission/reflection loss is frequency independent. This assumption is reasonable in the far-field situation and makes the transmission/reflection coefficient irrelevant with the Q estimation result. However, in the near-surface context, this assumption is invalid because the seismic wave propagates in the form of spherical wave in the real seismic surveys and the spherical-wave transmission/reflection coefficient is frequency dependent. As a result, deviation will exist. In this paper, the influence of the spherical-wave effect on the Q estimation in the near-surface context was proved in both synthetic data and field data for the first time, and it was found that the deviation due to the sphericalwave effect is of order comparable to the intrinsic attenuation. The compensation method based on the forward modeling is then proposed to correct this deviation, and the effectiveness of the proposed method is proved by the reasonable estimated results of both synthetic data and field data example. These results raise caution for the interpretation of the extracted Q in the near-surface context if they do not account for the spherical-wave effect and point to the necessity of incorporating a frequency-dependent term in the frequency-spectrum-based method when applied to the Q estimation in the near surface.

5

Multi sparsity based spectral attributes for discontinuity detection

Wang Tieyi, Yuan Sanyi, Yang Shan, Liu Bingyang, Wang Shangxu

Acta Geophysica

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2019

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Vol. 67, no. 4

1059--1069

EN

Structural and stratigraphic discontinuities, such as faults and channels, generally contribute to the construction of traps and reservoirs. Spectral decomposition can utilize the sensitivities of different frequency components to different geological conditions to identify these geological anomalies. The sparse inverse spectral decomposition (SISD) involves a sparse constraint of time–frequency spectra, and one critical parameter is the sparsity which determines the time–frequency resolution. A small sparsity gives a low temporal resolution result that cannot be used for thin-bed detection. Conversely, a large sparsity provides a high-resolution result, but it may lose weak refection signals. The complex geological conditions in the subsurface will lead to some difficulties in detecting the discontinuities by using the SISD method with a fixed sparsity. To address this issue, we propose multi-sparsity-based spectral attributes by fusing the amplitude spectra results of three different sparsities to detect subsurface discontinuities. Compared with the fixed sparsity, the multi-sparsity-based spectral attributes can detect more geological details and highlight geological edges more clearly. The application on a 3D real data with an area of 230 km2 from deep formation in Northwest China exhibits its effectiveness in discontinuity detection. The proposed method can detect the weak or small hidden geological details more and better than the fixed sparsity method, suggesting that it may serve as a future tool for detecting the distribution of geological abnormalities in subsurface.