Broadband seismic amplitude variation with offset inversion

Geophysics ◽  
2017 ◽  
Vol 82 (3) ◽  
pp. M43-M53 ◽  
Author(s):  
Zhaoyun Zong ◽  
Kun Li ◽  
Xingyao Yin ◽  
Ming Zhu ◽  
Jiayuan Du ◽  
...  
Keyword(s):  
Bayesian Inference ◽  
Frequency Domain ◽  
Seismic Data ◽  
Low Frequency ◽  
Well Log ◽  
Complex Frequency ◽  
Elastic Parameters ◽  
Log Data ◽  
Avo Inversion ◽  
Frequency Components

Seismic amplitude variation with offset (AVO) inversion is well-known as a popular and pragmatic tool used for the prediction of elastic parameters in the geosciences. Low frequencies missing from conventional seismic data are conventionally recovered from other geophysical information, such as well-log data, for estimating the absolute rock properties, which results in biased inversion results in cases of complex heterogeneous geologic targets or plays with sparse well-log data, such as marine or deep stratum. Broadband seismic data bring new opportunities to estimate the low-frequency components of the elastic parameters without well-log data. We have developed a novel AVO inversion approach with the Bayesian inference for broadband seismic data. The low-frequency components of the elastic parameters are initially estimated with the proposed broadband AVO inversion approach with the Bayesian inference in the complex frequency domain because seismic inversion in the complex frequency domain is helpful to recover the long-wavelength structures of the elastic models. Gaussian and Cauchy probability distribution density functions are used for the likelihood function and the prior information of model parameters, respectively. The maximum a posteriori probability solution is resolved to estimate the low-frequency components of the elastic parameters in the complex frequency domain. Furthermore, with those low-frequency components as initial models and constraints, the conventional AVO inversion method with the Bayesian inference in the time domain is further implemented to estimate the final absolute elastic parameters. Synthetic and field data examples demonstrate that the proposed AVO inversion in the complex frequency domain is able to predict the low-frequency components of elastic parameters well, and that those low-frequency components set a good foundation for the final estimation of the absolute elastic parameters.

scholarly journals Multi-scale time-frequency domain full waveform inversion with a weighted local correlation-phase misfit function

2019 ◽  
Vol 16 (6) ◽  
pp. 1017-1031 ◽  
Author(s):  
Yong Hu ◽  
Liguo Han ◽  
Rushan Wu ◽  
Yongzhong Xu
Keyword(s):  
Frequency Domain ◽  
Seismic Data ◽  
Waveform Inversion ◽  
Low Frequency ◽  
Full Waveform Inversion ◽  
Local Correlation ◽  
Time Frequency ◽  
Model Dependence ◽  
Full Waveform ◽  
Frequency Components

Abstract Full Waveform Inversion (FWI) is based on the least squares algorithm to minimize the difference between the synthetic and observed data, which is a promising technique for high-resolution velocity inversion. However, the FWI method is characterized by strong model dependence, because the ultra-low-frequency components in the field seismic data are usually not available. In this work, to reduce the model dependence of the FWI method, we introduce a Weighted Local Correlation-phase based FWI method (WLCFWI), which emphasizes the correlation phase between the synthetic and observed data in the time-frequency domain. The local correlation-phase misfit function combines the advantages of phase and normalized correlation function, and has an enormous potential for reducing the model dependence and improving FWI results. Besides, in the correlation-phase misfit function, the amplitude information is treated as a weighting factor, which emphasizes the phase similarity between synthetic and observed data. Numerical examples and the analysis of the misfit function show that the WLCFWI method has a strong ability to reduce model dependence, even if the seismic data are devoid of low-frequency components and contain strong Gaussian noise.

Acoustic impedance estimation from combined harmonic reconstruction and interval velocity

Geophysics ◽  
2019 ◽  
Vol 84 (3) ◽  
pp. R385-R400
Author(s):  
Luca Bianchin ◽  
Emanuele Forte ◽  
Michele Pipan
Keyword(s):  
Velocity Field ◽  
Seismic Data ◽  
Acoustic Impedance ◽  
Low Frequency ◽  
Reconstruction Method ◽  
Well Log ◽  
Data Set ◽  
Low Frequencies ◽  
Interval Velocity ◽  
Frequency Components

Low-frequency components of reflection seismic data are of paramount importance for acoustic impedance (AI) inversion, but they typically suffer from a poor signal-to-noise ratio. The estimation of the low frequencies of AI can benefit from the combination of a harmonic reconstruction method (based on autoregressive [AR] models) and a seismic-derived interval velocity field. We have developed the construction of a convex cost function that accounts for the velocity field, together with geologic a priori information on AI and its uncertainty, during the AR reconstruction of the low frequencies. The minimization of this function allows one to reconstruct sensible estimates of low-frequency components of the subsurface reflectivity, which lead to an estimation of AI model via a recursive formulation. In particular, the method is suited for an initial and computationally inexpensive assessment of the absolute value of AI even when no well-log data are available. We first tested the method on layered synthetic models, then we analyzed its applicability and limitations on a real marine seismic data set that included tomographic velocity information. Despite a strong trace-to-trace variability in the results, which could partially be mitigated by multitrace inversion, the method demonstrates its capability to highlight lateral variations of AI that cannot be detected when the low frequencies only come from well-log information.

Brittleness index calculation based on amplitude-variation-with-offset inversion for coal-bed methane reservoir: A case study of the Qinshui Basin, China

2020 ◽  
Vol 8 (1) ◽  
pp. SA63-SA72
Author(s):  
Wu Haibo ◽  
Cheng Yan ◽  
Zhang Pingsong ◽  
Dong Shouhua ◽  
Huang Yaping
Keyword(s):  
Seismic Data ◽  
Brittleness Index ◽  
Coal Bed ◽  
Coal Bed Methane ◽  
Well Log ◽  
Amplitude Variation ◽  
Frequency Model ◽  
Log Data ◽  
Amplitude Variation With Offset ◽  
Avo Inversion

The brittleness index (BI) is an important parameter for coal-bed methane (CBM) reservoir fracturing characterization. Most published studies have relied on petrophysical and well-log data to estimate the geomechanical properties of reservoir rocks. The major drawback of such methods is the lack of control away from well locations. Therefore, we have developed a method of combining BI calculation from well logs with that inverted from 3D seismic data to overcome the limitation. A real example is given here to indicate the workflow. A traditional amplitude-variation-with-offset (AVO) inversion was conducted first. BI for the CBM reservoir was then calculated from the Lamé constants inverted from prestack seismic data through a traditional AVO inversion method. We build an initial low-frequency model based on the well-log data. Comparison of the seismic inverted BI at the target reservoir and BI extracted from the well-log data showed satisfactory results. This method has been proved to be efficient and effective enough at identifying BI sweet spots in CBM reservoirs.

scholarly journals Deep learning for low-frequency extrapolation from multioffset seismic data

Geophysics ◽  
2019 ◽  
Vol 84 (6) ◽  
pp. R989-R1001 ◽  
Author(s):  
Oleg Ovcharenko ◽  
Vladimir Kazei ◽  
Mahesh Kalita ◽  
Daniel Peter ◽  
Tariq Alkhalifah
Keyword(s):  
Deep Learning ◽  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Waveform Inversion ◽  
Low Frequency ◽  
Low Frequencies ◽  
Seismic Surveys ◽  
Full Waveform ◽  
Frequency Components ◽  
Marine Seismic

Low-frequency seismic data are crucial for convergence of full-waveform inversion (FWI) to reliable subsurface properties. However, it is challenging to acquire field data with an appropriate signal-to-noise ratio in the low-frequency part of the spectrum. We have extrapolated low-frequency data from the respective higher frequency components of the seismic wavefield by using deep learning. Through wavenumber analysis, we find that extrapolation per shot gather has broader applicability than per-trace extrapolation. We numerically simulate marine seismic surveys for random subsurface models and train a deep convolutional neural network to derive a mapping between high and low frequencies. The trained network is then tested on sections from the BP and SEAM Phase I benchmark models. Our results indicate that we are able to recover 0.25 Hz data from the 2 to 4.5 Hz frequencies. We also determine that the extrapolated data are accurate enough for FWI application.

Iterative P-wave velocity inversion using Markov chain Monte Carlo method

2020 ◽  
Author(s):  
Hyunggu Jun ◽  
Hyeong-Tae Jou ◽  
Han-Joon Kim ◽  
Sang Hoon Lee
Keyword(s):  
Wave Velocity ◽  
Seismic Data ◽  
Velocity Structure ◽  
Low Frequency ◽  
P Wave ◽  
Velocity Analysis ◽  
Seismic Section ◽  
Traveltime Tomography ◽  
P Wave Velocity ◽  
Frequency Components

<p>Imaging the subsurface structure through seismic data needs various information and one of the most important information is the subsurface P-wave velocity. The P-wave velocity structure mainly influences on the location of the reflectors during the subsurface imaging, thus many algorithms has been developed to invert the accurate P-wave velocity such as conventional velocity analysis, traveltime tomography, migration velocity analysis (MVA) and full waveform inversion (FWI). Among those methods, conventional velocity analysis and MVA can be widely applied to the seismic data but generate the velocity with low resolution. On the other hands, the traveltime tomography and FWI can invert relatively accurate velocity structure, but they essentially need long offset seismic data containing sufficiently low frequency components. Recently, the stochastic method such as Markov chain Monte Carlo (McMC) inversion was applied to invert the accurate P-wave velocity with the seismic data without long offset or low frequency components. This method uses global optimization instead of local optimization and poststack seismic data instead of prestack seismic data. Therefore, it can avoid the problem of the local minima and limitation of the offset. However, the accuracy of the poststack seismic section directly affects the McMC inversion result. In this study, we tried to overcome the dependency of the McMC inversion on the poststack seismic section and iterative workflow was applied to the McMC inversion to invert the accurate P-wave velocity from the simple background velocity and inaccurate poststack seismic section. The numerical test showed that the suggested method could successfully invert the subsurface P-wave velocity.</p>

Seismic reservoir characterization of the Bone Spring and Wolfcamp Formations in the Delaware Basin: Challenges and uncertainty in characterization using rock physics — A case study: Part 2

2020 ◽  
Vol 8 (4) ◽  
pp. T1057-T1069
Author(s):  
Ritesh Kumar Sharma ◽  
Satinder Chopra ◽  
Larry Lines
Keyword(s):  
Seismic Data ◽  
Reservoir Characterization ◽  
Rock Physics ◽  
Well Log ◽  
Data Set ◽  
Frequency Model ◽  
Log Data ◽  
Delaware Basin ◽  
Seismic Reservoir

The discrimination of fluid content and lithology in a reservoir is important because it has a bearing on reservoir development and its management. Among other things, rock-physics analysis is usually carried out to distinguish between the lithology and fluid components of a reservoir by way of estimating the volume of clay, water saturation, and porosity using seismic data. Although these rock-physics parameters are easy to compute for conventional plays, there are many uncertainties in their estimation for unconventional plays, especially where multiple zones need to be characterized simultaneously. We have evaluated such uncertainties with reference to a data set from the Delaware Basin where the Bone Spring, Wolfcamp, Barnett, and Mississippian Formations are the prospective zones. Attempts at seismic reservoir characterization of these formations have been developed in Part 1 of this paper, where the geologic background of the area of study, the preconditioning of prestack seismic data, well-log correlation, accounting for the temporal and lateral variation in the seismic wavelets, and building of robust low-frequency model for prestack simultaneous impedance inversion were determined. We determine the challenges and the uncertainty in the characterization of the Bone Spring, Wolfcamp, Barnett, and Mississippian sections and explain how we overcame those. In the light of these uncertainties, we decide that any deterministic approach for characterization of the target formations of interest may not be appropriate and we build a case for adopting a robust statistical approach. Making use of neutron porosity and density porosity well-log data in the formations of interest, we determine how the type of shale, volume of shale, effective porosity, and lithoclassification can be carried out. Using the available log data, multimineral analysis was also carried out using a nonlinear optimization approach, which lent support to our facies classification. We then extend this exercise to derived seismic attributes for determination of the lithofacies volumes and their probabilities, together with their correlations with the facies information derived from mud log data.

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