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Seismic data reconstruction based on low dimensional manifold model

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Petroleum Science 2022年第2期19卷 518-533页

作者： Nan-ying Lan Fan-Chang Zhang xing-yao yin School of Geosciences China University of Petroleum(East China)QingdaoShandong 266580China

Seismic data reconstruction is an essential and yet fundamental step in seismic data processing workflow,which is of profound significance to improve migration imaging quality,multiple suppression effect,and seismic inversion *** methods play a central role in solving the underdetermined inverse problem of seismic data *** this paper,a novel regularization approach is proposed,the low dimensional manifold model(LDMM),for reconstructing the missing seismic *** work relies on the fact that seismic patches always occupy a low dimensional ***,we exploit the dimension of the seismic patches manifold as a regularization term in the reconstruction problem,and reconstruct the missing seismic data by enforcing low dimensionality on this *** crucial procedure of the proposed method is to solve the dimension of the patches *** this,we adopt an efficient dimensionality calculation method based on low-rank approximation,which provides a reliable safeguard to enforce the constraints in the reconstruction *** experiments performed on synthetic and field seismic data demonstrate that,compared with the curvelet-based sparsity-promoting L1-norm minimization method and the multichannel singular spectrum analysis method,the proposed method obtains state-of-the-art reconstruction results.

关键词： Seismic data reconstruction Low dimensional manifold model Regularization Low-rank approximation

Monitoring the change in horizontal stress with multi-wave time-lapse seismic response based on nonlinear elasticity theory

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Petroleum Science 2023年第2期20卷 815-826页

作者： Fu-Bin Chen Zhao-Yun Zong xing-yao yin School of Geosciences China University of Petroleum East ChinaQingdaoShandong266580China Laoshan Laboratory QingdaoShandong266580China Shandong Provincial Key Laboratory of Deep Oil and Gas QingdaoShandong266580China

Monitoring the change in horizontal stress from the geophysical data is a tough challenge, and it has a crucial impact on broad practical scenarios which involve reservoir exploration and development, carbon dioxide (CO_(2)) injection and storage, shallow surface prospecting and deep-earth structure description. The change in in-situ stress induced by hydrocarbon production and localized tectonic movements causes the changes in rock mechanic properties (e.g. wave velocities, density and anisotropy) and further causes the changes in seismic amplitudes, phases and travel times. In this study, the nonlinear elasticity theory that regards the rock skeleton (solid phase) and pore fluid as an effective whole is used to characterize the effect of horizontal principal stress on rock overall elastic properties and the stress-dependent anisotropy parameters are therefore formulated. Then the approximate P-wave, SV-wave and SH-wave angle-dependent reflection coefficient equations for the horizontal-stress-induced anisotropic media are proposed. It is shown that, on the different reflectors, the stress-induced relative changes in reflectivities (i.e., relative difference) of elastic parameters (i.e., P- and S-wave velocities and density) are much less than the changes in contrasts of anisotropy parameters. Therefore, the effects of stress change on the reflectivities of three elastic parameters are reasonably neglected to further propose an AVO inversion approach incorporating P-, SH- and SV-wave information to estimate the change in horizontal principal stress from the corresponding time-lapse seismic data. Compared with the existing methods, our method eliminates the need for man-made rock-physical or fitting parameters, providing more stable predictive power. 1D test illustrates that the estimated result from time-lapse P-wave reflection data shows the most reasonable agreement with the real model, while the estimated result from SH-wave reflection data shows the largest bia

关键词： Monitoring change in horizontal stress Multi-wave reflection coefficients Nonlinear elasticity theory Time-lapse seismic data

Bayesian seismic multi-scale inversion in complex Laplace mixed domains

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Petroleum Science 2017年第4期14卷 694-710页

作者： Kun Li xing-yao yin Zhao-Yun Zong School of Geosciences China University of Petroleum (East China) Laboratory for Marine Mineral Resources Qingdao National Laboratory for Marine Science and Technology

Seismic inversion performed in the time or frequency domain cannot always recover the long-wavelength background of subsurface parameters due to the lack of low-frequency seismic records. Since the low-frequency response becomes much richer in the Laplace mixed domains, one novel Bayesian impedance inversion approach in the complex Laplace mixed domains is established in this study to solve the model dependency problem. The derivation of a Laplace mixed-domain formula of the Robinson convolution is the first step in our work. With this formula, the Laplace seismic spectrum, the wavelet spectrum and time-domain reflectivity are joined together. Next, to improve inversion stability, the object inversion function accompanied by the initial constraint of the linear increment model is launched under a Bayesian framework. The likelihood function and prior probability distribution can be combined together by Bayesian formula to calculate the posterior probability distribution of subsurface parameters. By achieving the optimal solution corresponding to maximum posterior probability distribution, the low-frequency background of subsurface parameters can be obtained successfully. Then, with the regularization constraint of estimated low frequency in the Laplace mixed domains, multi-scale Bayesian inversion inthe pure frequency domain is exploited to obtain the absolute model parameters. The effectiveness, anti-noise capability and lateral continuity of Laplace mixed-domain inversion are illustrated by synthetic tests. Furthermore,one field case in the east of China is discussed carefully with different input frequency components and different inversion algorithms. This provides adequate proof to illustrate the reliability improvement in low-frequency estimation and resolution enhancement of subsurface parameters, in comparison with conventional Bayesian inversion in the frequency domain.

关键词： Low-frequency Complex mixed-domain Laplace inversion Bayesian estimation Multi-scale inversion

Machine learning seismic reservoir prediction method based on virtual sample generation

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Petroleum Science 2021年第6期18卷 1662-1674页

作者： Kai-Heng Sang xing-yao yin Fan-Chang Zhang School of Geosciences China University of Petroleum(East China)QingdaoShandong 266580China

Seismic reservoir prediction plays an important role in oil exploration and *** the progress of artificial intelligence,many achievements have been made in machine learning seismic reservoir ***,due to the factors such as economic cost,exploration maturity,and technical limitations,it is often difficult to obtain a large number of training samples for machine *** this case,the prediction accuracy cannot meet the *** overcome this shortcoming,we develop a new machine learning reservoir prediction method based on virtual sample *** this method,the virtual samples,which are generated in a high-dimensional hypersphere space,are more consistent with the original data ***,at the stage of model building after virtual sample generation,virtual samples screening and model iterative optimization are used to eliminate noise samples and ensure the rationality of virtual *** proposed method has been applied to standard function data and real seismic *** results show that this method can improve the prediction accuracy of machine learning significantly.

关键词： Virtual sample Machine learning Reservoir prediction Hypersphere characteristic equation

Pre-stack basis pursuit seismic inversion for brittleness of shale

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Petroleum Science 2015年第4期12卷 618-627页

作者： xing-yao yin Xiao-Jing Liu Zhao-Yun Zong School of Geosciences China University of Petroleum (Huadong)

Brittleness of rock plays a significant role in exploration and development of shale gas reservoirs. Young＇s modulus and Poisson＇s ratio are the key param- eters for evaluating the rock brittleness in shale gas exploration because their combination relationship can quantitatively characterize the rock brittleness. The high- value anomaly of Young＇s modulus and the low-value anomaly of Poisson＇s ratio represent high brittleness of shale. The technique of pre-stack amplitude variation with angle inversion allows geoscientists to estimate Young＇s modulus and Poisson＇s ratio from seismic data. A model constrained basis pursuit inversion method is proposed for stably estimating Young＇s modulus and Poisson＇s ratio. Test results of synthetic gather data show that Young＇s modulus and Poisson＇s ratio can be estimated reasonably. With the novel method, the inverted Young＇s modulus and Poisson＇s ratio of real field data focus the layer boundaries better, which is helpful for us to evaluate the brittleness of shale gas reservoirs. The results of brittleness evaluation show a good agreement with the results of well interpretation.

关键词： Brittleness Shale gas Amplitude variationwith angle Basis pursuit Bayesian framework

Complex spherical-wave elastic inversion using amplitude and phase reflection information

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Petroleum Science 2022年第3期19卷 1065-1084页

作者： Guang-Sen Cheng xing-yao yin Zhao-Yun Zong Ya-Ming Yang School of Geosciences China University of Petroleum(East China)QingdaoShandong 266580China Pilot National Laboratory for Marine Science and Technology(Qingdao) QingdaoShandong 266580China Shandong Provincial Key Laboratory of Deep Oil and Gas QingdaoShandong 266580China

Unlike the real-valued plane wave reflection coefficient(PRC)at the pre-critical incident angles,the frequency-and depth-dependent spherical-wave reflection coefficient(SRC)is more accurate and always a complex value,which contains more reflection amplitude and phase *** near field,the imaginary part of complex SRC(phase)cannot be ignored,but it is rarely considered in seismic *** promote the practical application of spherical-wave seismic inversion,a novel spherical-wave inversion strategy is *** complex-valued spherical-wave synthetic seismograms can be obtained by using a simple harmonic superposition *** is assumed that geophone can only record the real part of complex-valued *** imaginary part can be further obtained by the Hilbert transform *** also propose the concept of complex spherical-wave elastic impedance(EI)and the complex spherical-wave EI ***,a novel complex spherical-wave EI inversion approach is proposed,which can fully use the reflection information of amplitude,phase,and *** the inverted complex spherical-wave EI,the velocities and density can be further *** data and field data examples show that the elastic parameters can be reasonably estimated,which illustrate the potential of our spherical-wave inversion approach in practical applications.

关键词： Complex seismic traces Spherical-wave theory Reflection amplitude and phase Elastic impedance Bayesian inversion

Simultaneous denoising and resolution enhancement of seismic data based on elastic convolution dictionary learning

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Petroleum Science 2023年第4期20卷 2127-2140页

作者： Nan-ying Lan Fan-Chang Zhang Kai-Heng Sang xing-yao yin School of Geosciences China University of Petroleum(East China)QingdaoShandong266580China

Enhancing seismic resolution is a key component in seismic data processing, which plays a valuable role in raising the prospecting accuracy of oil reservoirs. However, in noisy situations, existing resolution enhancement methods are difficult to yield satisfactory processing outcomes for reservoir characterization. To solve this problem, we develop a new approach for simultaneous denoising and resolution enhancement of seismic data based on convolution dictionary learning. First, an elastic convolution dictionary learning algorithm is presented to efficiently learn a convolution dictionary with stronger representation capability from the noisy data to be processed. Specifically, the algorithm introduces the elastic L1/2 norm as a sparsity constraint and employs a steepest gradient descent strategy to efficiently solve the frequency-domain linear system with substantial computational cost in a half-quadratic splitting framework. Then, based on the learned convolution dictionary, a weighted convolutional sparse representation paradigm is designed to encode the noisy data to acquire an optimal sparse approximation of the effective signal. Subsequently, a high-resolution dictionary with a broadband spectrum is constructed by the proposed parameter scaling strategy and matched filtering technique on the basis of atomic spectrum modeling. Finally, the optimal sparse approximation of the effective signal and the constructed high-resolution dictionary are used for data reconstruction to obtain the seismic signal with high resolution and high signal-to-noise ratio. Synthetic and field dataset examples are executed to check the effectiveness and reliability of the developed method. The results indicate that this method has a more competitive performance in seismic applications compared with the conventional deconvolution and spectral whitening methods.

关键词： Simultaneous denoising and resolution enhancement Elastic convolution dictionary learning Weighted convolutional sparse representation Matched filtering

Seismic AVO statistical inversion incorporating poroelasticity

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Petroleum Science 2020年第5期17卷 1237-1258页

作者： Kun Li xing-yao yin Zhao-Yun Zong Hai-Kun Lin School of Geosciences China University of Petroleum(East China)Qingdao 266580ShandongChina Laboratory for Marine Mineral Resources Qingdao National Laboratory for Marine Science and TechnologyQingdao 266071ShandongChina

Seismic amplitude variation with offset(AVO) inversion is an important approach for quantitative prediction of rock elasticity,lithology and fluid *** Biot-Gassmann's poroelasticity,an improved statistical AVO inversion approach is *** distinguish the influence of rock porosity and pore fluid modulus on AVO reflection coefficients,the AVO equation of reflection coefficients parameterized by porosity,rock-matrix moduli,density and fluid modulus is initially derived from Gassmann equation and critical porosity *** the analysis of the influences of model parameters on the proposed AVO equation,rock porosity has the greatest influences,followed by rock-matrix moduli and density,and fluid modulus has the least influences among these model ***,a statistical AVO stepwise inversion method is implemented to the simultaneous estimation of rock porosity,rock-matrix modulus,density and fluid ***,the Laplace probability model and differential evolution,Markov chain Monte Carlo algorithm is utilized for the stochastic simulation within Bayesian *** and field data examples demonstrate that the simultaneous optimizations of multiple Markov chains can achieve the efficient simulation of the posterior probability density distribution of model parameters,which is helpful for the uncertainty analysis of the inversion and sets a theoretical fundament for reservoir characterization and fluid discrimination.

关键词： Poroelasticity AVO inversion Statistical inversion Bayesian inference Seismic fluid discrimination

Fast pre-stack multi-channel inversion constrained by seismic reflection features

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Petroleum Science 2023年第4期20卷 2060-2074页

作者： Ya-Ming Yang xing-yao yin Kun Li Feng Zhang Jian-Hu Gao School of Geosciences China University of Petroleum(East China)QingdaoShandong266580China Pilot National Laboratory for Marine Science and Technology(Qingdao) QingdaoShandong266580China Shandong Provincial Key Laboratory of Deep Oil and Gas QingdaoShandong266580China China University of Petroleum-Beijing Beijing102249China Research Institute of Petroleum Exploration and Development-Northwest LanzhouGansu730020China

Classical multi-channel technology can significantly reduce the pre-stack seismic inversion uncertainty, especially for complex geology such as high dipping structures. However, due to the consideration of complex structure or reflection features, the existing multi-channel inversion methods have to adopt the highly time-consuming strategy of arranging seismic data trace-by-trace, limiting its wide application in pre-stack inversion. A fast pre-stack multi-channel inversion constrained by seismic reflection features has been proposed to address this issue. The key to our method is to re-characterize the reflection features to directly constrain the pre-stack inversion through a Hadamard product operator without rearranging the seismic data. The seismic reflection features can reflect the distribution of the stratum reflection interface, and we obtained them from the post-stack profile by searching the shortest local Euclidean distance between adjacent seismic traces. Instead of directly constructing a large-size reflection features constraint operator advocated by the conventional methods, through decomposing the reflection features along the vertical and horizontal direction at a particular sampling point, we have constructed a computationally well-behaved constraint operator represented by the vertical and horizontal partial derivatives. Based on the Alternating Direction Method of Multipliers (ADMM) optimization, we have derived a fast algorithm for solving the objective function, including Hadamard product operators. Compared with the conventional reflection features constrained inversion, the proposed method is more efficient and accurate, proved on the Overthrust model and a field data set.

关键词： Pre-stack multi-channel inversion Reflection features Fast optimization