Low-frequency data analysis and expansion

Zhang, Junhua; Zhang, Bin-Bin; Zhang, Zaijin; Liang, Hongxian; Ge, Da-Ming

doi:10.1007/s11770-015-0484-2

Cited by 19 publications

(3 citation statements)

References 18 publications

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“…Another factor that should be considered when introducing the constrained term in seismic inversion is the quality of observed data for the inverse application. Reliable inverted results also depend on the seismic data quality, and the corresponding low-frequency component affects the stability for seismic inversion (Zhang et al, 2015), which is always missed in actual observed data. Therefore, the solution of to this problem is how to build a good initial model that contains low-frequency information to constrain the inversion process.…”

Section: Introductionmentioning

confidence: 99%

Envelope-based constrained model for multiparameter pre-stack seismic inversion

Shan

Yang

et al. 2022

J Petrol Explor Prod Technol

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Pre-stack seismic inversion is an effective method to estimate multiparameter from seismic reflection data in the oil–gas reservoir exploration. In order to get a reliable inverted parameter result, a constrained model is commonly used to reduce the multi-solution in the seismic inversion. However, it is hard to get an appropriate constrained model by conventional well-log data interpolation or velocity field in offshore oil–gas exploration with sparse wells. Envelope inversion has been successfully applied to get a good initial model with low-frequency components in full-waveform inversion. In order to fully exploit the rich information contained in seismic data, we introduce envelope inversion into background modeling based on convolution theory and propose a multiparameter pre-stack seismic inversion with envelope prior constraint. Both inversion results of the model and field data indicate the effectiveness and adaptability of the proposed method in multiparameter estimation.

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Section: Introductionmentioning

confidence: 99%

Envelope-based constrained model for multiparameter pre-stack seismic inversion

Shan

Yang

et al. 2022

J Petrol Explor Prod Technol

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“…The low‐frequency signals of seismic data are important in seismic imaging and inversion to detect deep hydrocarbon reservoirs, enhance spatial resolution and build accurate velocity models (Goloshubin et al ., 2002, 2006; Korneev et al ., 2004; Zhang, 2011; ten Kroode et al ., 2013; Zhang et al ., 2015; Wang et al ., 2017; Ahmad et al ., 2017, 2019; Jun et al ., 2018; Yang and Zhu, 2018; Yuan et al ., 2019). When seismic waves penetrate the hydrocarbon‐bearing reservoirs, low‐frequency signals suffer less from scattering and intrinsic attenuation than high‐frequency components, because of small dispersion and attenuation effects.…”

Section: Introductionmentioning

confidence: 99%

A new energy density calculation method for reservoir delineation using seismic low‐frequency signal

Luo

Chen

et al. 2020

Geophysical Prospecting

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In comparison to high‐frequency signals, low‐frequency seismic signals suffer less from scattering and intrinsic attenuation during wave propagation, penetrate deeper strata and thus can provide more energy information related to the hydrocarbon reservoirs. Based on the asymptotic representation for the frequency‐dependent reflections in the fluid‐saturated pore‐elastic media, we first derive a novel equation of the reservoir energy density and present an efficient workflow to calculate the reservoir energy density using low‐frequency seismic data. Then, within a low‐frequency range (from 1 to 30 Hz), we construct an objective function to determine the optimal frequency, using the energy densities calculated from the post‐stack seismic traces close to the wells. Next, we can calculate the reservoir energy density using the instantaneous spectra of optimal frequency at the low‐frequency end of the seismic spectrum. Tests on examples for synthetic and field data demonstrate that the proposed reservoir energy density can produce high‐quality images for the fluid‐saturated reservoirs, and it produces less background artefacts caused by elastic layers. This method provides a new way to detect the location of hydrocarbon reservoirs and characterize their spatial distribution.

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“…As we know, the width of the main lobe of the wavelet depends on the relative high-frequency energy and broadening the low-frequency bandwidth can effectively reduce the amplitude of wavelet side lobes (Kroode et al 2013;Liu et al 2013). Considering the influence of low frequencies on seismic imaging and velocity analysis in contrast to high frequencies, less absorption, less scattering and better penetration of low frequencies illustrate their importance in the field of deep imaging technology (Pedersen and Becken 2005;Spjuth et al 2012;Cao and Chen 2014;Zhang et al 2015). Geophysicists still prioritize low-frequency regularizations in waveform, impedance, elastic and AVO inversion (Zong et al 2012;Kroode et al 2013;Li et al 2016a) to improve the stability of the inversion process and the rate of convergence to a precise earth model.…”

Section: Introductionmentioning

confidence: 99%

Bayesian seismic multi-scale inversion in complex Laplace mixed domains

Yin

Zong

2017

Pet. Sci.

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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 in the 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.

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Low-frequency data analysis and expansion

Cited by 19 publications

References 18 publications

Envelope-based constrained model for multiparameter pre-stack seismic inversion

Envelope-based constrained model for multiparameter pre-stack seismic inversion

A new energy density calculation method for reservoir delineation using seismic low‐frequency signal

Bayesian seismic multi-scale inversion in complex Laplace mixed domains

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