Amplitude variation with offset/angle of incidence (AVO/AVA) analysis is essential for hydrocarbon detection and reservoir characterization. Frequency-dependent AVO analysis plays an important role in seismic interpretation especially for the low-frequency seismic anomalies related to hydrocarbon reservoir. The diffusive-viscous model is used to explain these anomalies, but it does not consider the shear effects of rocks. In this work, we firstly extend the diffusive-viscous model to elastic case based on the mechanisms in a macroscopic porous medium. The elastic diffusive-viscous model describes attenuation of compressional and shear waves in a fluid-saturated medium and it reduces to the classic elastic wave equation in a special case. Then, we investigate the properties of reflection/transmission coefficients at an interface between two different elastic diffusive-viscous media. The reflection/transmission coefficients not only relate to the parameters of the media but also depend on the frequency. Two examples are given to analyze the dependence of the reflection/transmission coefficients on the frequency and incident angle at interfaces between gas-saturated sandstone and brine-saturated shale and between brine-saturated shale and oil-saturated sandstone. The results show that the magnitudes and phase angles of the reflection/transmission coefficients are significantly dependent on the frequency at lower frequency (<20 Hz). Finally, we apply the frequency-dependent reflection/transmission coefficients to the extended reflectivity method to model the propagation of the elastic diffusive-viscous wave in a layered medium. The modeling results show that the diffusive-viscous wave has strong amplitude attenuation and phase shift compared with those of elastic wave when the wave propagates across fluid-saturated layers.
Seismic reflections at an interface are often regarded as the variation of the acoustic impedance (product of seismic velocity and density) in a media. In fact, they can also be generated due to the difference in absorption of the seismic energy. In this work, we investigate the impacts of attenuation on seismic reflections based on the diffusive-viscous wave equation, which is used to investigate seismic attenuation and frequency-dependent seismic anomalies related to hydrocarbon reservoirs. The results show that the reflections are significantly affected by the diffusive attenuation but they are insensitive to the viscous attenuation in an acoustic dispersive medium. In an elastic dispersive medium, the attenuation parameter in P wave equation has a big impact on both PP and PS reflections, however, the attenuation parameter in S wave equation has little effect on the PP reflection but it strongly affects the PS reflections. Furthermore, the PP and PS reflections in the dispersive medium are dependent on the frequency, and the effect of attenuation on PP and PS reflections at lower frequencies is bigger than those at higher frequencies.
Estimating the elastic parameters from prestack inversion is of great importance for reservoir characterization. Conventional three-parameter prestack inversion methods rely heavily on well logs, and it is difficult to obtain reliable inversion results in situations with limited numbers of wells. Alternatively, we have developed a joint inversion strategy, integrating the advantages of post- and prestack inversion, to deal with the situation. First, due to the high signal-to-noise ratio of poststack seismic data, the high-precision acoustic impedance (AI) inversion is conducted. Second, the exact Zoeppritz equation is used to establish the objective function of the prestack inversion. To better constrain the elastic parameters (P- and S-wave velocities and density), a new similarity measurement criterion, the gradient structure similarity (GSS), is defined to describe the structural similarity between the prestack inversion results and the inverted AI from the poststack inversion. Third, the LM optimization algorithm is used to solve the nonlinear objective function. Through the model test, we verify the effectiveness of our GSS regularization scheme. Some synthetic and field examples find that our method can provide more stable and accurate inverted results relative to the conventional prestack inversion methods.
Time–frequency (T–F) analysis has powerful applications in various fields such as geoscience and engineering. Seismograms are distorted by dispersion and attenuation in such a way that they alter their amplitude and phase spectra. Velocity dispersion is usually neglected in the conventional seismic data processing. However, it has a severe effect on seismic data processing if dispersion is intense in high-attenuation media. Most of the studies on dispersion are theoretical models and laboratory measurements, whereas velocity dispersion analysis directly from field data is rare. In this letter, a method is proposed to estimate the dispersion of vertical seismic profile (VSP) data based on continuous wavelet transform (CWT) in time–frequency domain. The Morlet wavelet, three-parameter wavelet (TPW), and Cauchy wavelet are chosen and compared as mother wavelets in CWT to determine the variations of velocity with frequency. Then, the detailed process of extracting velocity dispersion of propagating waves in a dissipative medium is proposed by using their T–F spectra. Finally, the synthetic and field VSP data are used to demonstrate the validity of the proposed method. The results indicate that the velocity dispersion in synthetic data estimated by TPW and Cauchy wavelet generally match well with the theoretical values while it is not the case for the Morlet wavelet. The velocity dispersion of field data is weak and the estimated velocity basically matches well with the logging data.
Distributed acoustic sensing (DAS) is one of the most popular sensors for seismic acquisition. Compared with traditional seismic acquisition technology, DAS has the advantages of full-well coverage, high density, high efficiency, high sensitivity, low cost, strong resistance to high temperature and high pressure, and anti-electromagnetic field interference. However, the DAS vertical seismic profile (VSP) data are contaminated by strong noise interference, which brings challenges for practical applications and difficulties to seismic inversion and interpretation. A deep learning method named U-net with Global Context Block and Attention Block (GC-AB-Unet) is proposed to suppress the background noise and increase the data quality for DAS-VSP records without knowing any prior information. In GC-AB-Unet, several dropout layers are added to the U-net to avoid overfitting. Meanwhile, to speed up the network training, the residual units are set as the output of the network. Furthermore, GC-Block is introduced for better capturing shallow and deep features by extracting global context information. In addition, Attention Block is used to emphasize seismic event features and restrain irrelevant details in seismic data. We also construct a training dataset by utilizing the synthetic data and real noise of DAS-VSP data. The denoising results for both synthetic data and field DAS-VSP data show that compared to original U-net and Damped Rank Reduction (DRR) method, the GC-AB-Unet network is able to preserve the effective signals with almost no energy leakage while suppressing a large amount of background noise.
The producing oil-field need the second/third time seismic data acquisition to evaluate the remaining production of crude oil. In the repeated data acquisition, the non-random well-pump noise affects the quality of seismic data. In order to know the characteristics of well-pump noise, we design a small single-point receiver array, and use this array to receive sufficient time duration of the typical wellpump noise. After analyzing the obtained well-pump noise, we find it has some characteristic differences with reflections, such as limited bandwidth, locating in lowfrequency area, low velocity, dispersion and bad spatial correlation. Taking into account these differences, we propose an iterative method for well-pump noise attenuation in the time-frequency domain. By applying the proposed method to synthetic signal and real field data, we demonstrate the superior performance of our method. Presentation Date: Wednesday, September 27, 2017 Start Time: 11:25 AM Location: 360A Presentation Type: ORAL
Abstract The diffusive viscous (DV) model is a useful tool for interpreting low-frequency seismic attenuation and the influence of fluid saturation on frequency-dependent reflections. Among present methods for the numerical solution of the corresponding DV wave equation, the finite-difference frequency-domain (FDFD) method with complex-valued adaptive coefficients (CVAC) has the advantage of efficiently suppressing both numerical dispersion and numerical attenuation. In this research, the FDFD method with CVAC is first generalized to a 3D DV equation. In addition, the current calculation of CVAC involves the numerical integration of propagation angles, conjugate gradient (CG) iterative optimization, and the sequential selection of initial values, which is difficult and inefficient for implementation. An improved method is developed for calculating CVAC, in which a complex-valued least-squares problem is constructed by substituting the 3D complex-valued plane-wave solutions into the FDFD scheme. The QR-decomposition method is used to efficiently solve the least-squares problem. Numerical dispersion and attenuation analyses reveal that the FDFD method with CVAC requires ∼2.5 spatial points in a wavelength within a dispersion deviation of 1% and an attenuation deviation of 10% for the 3D DV equation. An analytic solution for 3D DV wave equation in homogeneous media is proposed to verify the effectiveness of the proposed method. Numerical examples also demonstrate that the FDFD method with CVAC can obtain accurate wavefield modelling results for 3D DV models with a limited number of spatial points in a wavelength, and the FDFD method with QR-based CVAC requires less computational time than the FDFD method with CG-based CVAC.
Seismic numerical modeling is a technique for simulating the wave propagation in the earth. The aim is to predict the seismogram, given an assumed structure of the subsurface. Real subsurface structure is complex and often multi-phase media because of fluid saturation, so the commonly used models such as acoustic, elastic media, etc., cannot characterize the information of real subsurface structure. The anelastic attenuation occurs when the waves propagate in fluid-saturated media. The diffusive-viscous model can be used to describe the attenuation of seismic waves propagating in fluid-saturated rocks, and it is also used to investigate the relationship between the frequency dependence of reflections and fluid saturation in a porous medium. In this paper, we derive the finite-difference scheme for the diffusive-viscous wave equation and simulate the propagation of seismic waves in fluid-saturated media based on the diffusive-viscous model, using the flux-corrected transport–finite-difference method (FCT–FDM). The numerical results show that the propagating waves in fluid-saturated media greatly attenuate by comparing with those of acoustic case.
Seismic data are often contaminated by random noise or even more complex types of noise, resulting in poor quality of seismic data with low signal-to-noise. Seismic random noise suppression is a crucial procedure in seismic data processing. Deep learning methods have been successfully applied to suppress seismic random noise. In this study, we propose a U-net based deep learning method to suppress seismic random noise. We add several dropout layers to the U-net to effectively avoid overfitting and set the output of the network as the residual units to enhance the training efficiency of our network. Moreover, the cosine similarity index is incorporated into the loss function to reserve the lateral continuity of geological structures. The denoising results of synthetic seismic data and field VSP data demonstrate that the proposed network has great performance in seismic random noise suppression in terms of both quantitative metrics and intuitive effects.
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