Reflectivity inversion for attenuated seismic data: Physical modeling and field data experiments

Geophysics ◽  
2016 ◽  
Vol 81 (1) ◽  
pp. T11-T24 ◽  
Author(s):  
Xintao Chai ◽  
Shangxu Wang ◽  
Jianxin Wei ◽  
Jingnan Li ◽  
Hanjun Yin
Keyword(s):  
Physical Model ◽  
Seismic Data ◽  
Physical Modeling ◽  
P Wave ◽  
Ratio Method ◽  
Blind Test ◽  
Data Set ◽  
Text Input ◽  
Text Filtering ◽  
Reflectivity Inversion

We have developed a method of nonstationary sparse reflectivity inversion (NSRI) that directly retrieves the reflectivity series from nonstationary seismic data without the intrinsic instability associated with inverse [Formula: see text] filtering methods. We have investigated the NSRI performance in the presence of input error (e.g., the phase shift and the peak frequency of the wavelet), which determined that NSRI results are reasonable in the case of moderate error. NSRI was then applied to data collected from a laboratory physical model made of highly attenuating media, for which true reflectivities were known, but the wave propagation and the [Formula: see text] filtering mechanism were not. Analysis of data from the physical model, therefore, represented a blind test for evaluating the effectiveness and accuracy of NSRI. The physical model had a specified spatial scale of 1:5000 (relative to the field scale) and an approximate 1:1 velocity scale. Because the [Formula: see text] input is required by NSRI, attenuation of the P-wave was measured in the laboratory at ultrasonic frequencies with a pulse transmission technique and the spectral ratio method. Although multiples, side reflections from the model boundaries, diffractions, and other event types were clearly observed from the raw data, no preprocessing was done to avoid affecting the [Formula: see text] filtering effects. Results from the physical modeling data demonstrated that the derived formula for an attenuated seismogram was correct and estimated [Formula: see text] values were reliable. The functions and advantages of NSRI were confirmed. The [Formula: see text] compensated seismogram generated by NSRI was superior to that generated using gain-limited inverse [Formula: see text] filtering. We have also investigated NSRI’s capabilities in analyzing a marine data set from the Gulf of Suez. These examples provided a basis for discussing assumptions and limitations of NSRI.

Sparse reflectivity inversion for nonstationary seismic data with surface-related multiples: Numerical and field-data experiments

Geophysics ◽  
2017 ◽  
Vol 82 (3) ◽  
pp. R199-R217 ◽  
Author(s):  
Xintao Chai ◽  
Shangxu Wang ◽  
Genyang Tang
Keyword(s):  
Seismic Data ◽  
Resolution Enhancement ◽  
Synthetic Data ◽  
Data Sets ◽  
Data Set ◽  
Anelastic Attenuation ◽  
Seismic Resolution ◽  
Text Filtering ◽  
The Stability ◽  
Reflectivity Inversion

Seismic data are nonstationary due to subsurface anelastic attenuation and dispersion effects. These effects, also referred to as the earth’s [Formula: see text]-filtering effects, can diminish seismic resolution. We previously developed a method of nonstationary sparse reflectivity inversion (NSRI) for resolution enhancement, which avoids the intrinsic instability associated with inverse [Formula: see text] filtering and generates superior [Formula: see text] compensation results. Applying NSRI to data sets that contain multiples (addressing surface-related multiples only) requires a demultiple preprocessing step because NSRI cannot distinguish primaries from multiples and will treat them as interference convolved with incorrect [Formula: see text] values. However, multiples contain information about subsurface properties. To use information carried by multiples, with the feedback model and NSRI theory, we adapt NSRI to the context of nonstationary seismic data with surface-related multiples. Consequently, not only are the benefits of NSRI (e.g., circumventing the intrinsic instability associated with inverse [Formula: see text] filtering) extended, but also multiples are considered. Our method is limited to be a 1D implementation. Theoretical and numerical analyses verify that given a wavelet, the input [Formula: see text] values primarily affect the inverted reflectivities and exert little effect on the estimated multiples; i.e., multiple estimation need not consider [Formula: see text] filtering effects explicitly. However, there are benefits for NSRI considering multiples. The periodicity and amplitude of the multiples imply the position of the reflectivities and amplitude of the wavelet. Multiples assist in overcoming scaling and shifting ambiguities of conventional problems in which multiples are not considered. Experiments using a 1D algorithm on a synthetic data set, the publicly available Pluto 1.5 data set, and a marine data set support the aforementioned findings and reveal the stability, capabilities, and limitations of the proposed method.

scholarly journals Semiglobal viscoacoustic full-waveform inversion

Geophysics ◽  
2019 ◽  
Vol 84 (2) ◽  
pp. R271-R293 ◽  
Author(s):  
Nuno V. da Silva ◽  
Gang Yao ◽  
Michael Warner
Keyword(s):  
Physical Properties ◽  
Wave Velocity ◽  
Seismic Data ◽  
Waveform Inversion ◽  
P Wave ◽  
Full Waveform Inversion ◽  
Data Set ◽  
Intrinsic Attenuation ◽  
Full Waveform ◽  
P Wave Velocity

Full-waveform inversion deals with estimating physical properties of the earth’s subsurface by matching simulated to recorded seismic data. Intrinsic attenuation in the medium leads to the dispersion of propagating waves and the absorption of energy — media with this type of rheology are not perfectly elastic. Accounting for that effect is necessary to simulate wave propagation in realistic geologic media, leading to the need to estimate intrinsic attenuation from the seismic data. That increases the complexity of the constitutive laws leading to additional issues related to the ill-posed nature of the inverse problem. In particular, the joint estimation of several physical properties increases the null space of the parameter space, leading to a larger domain of ambiguity and increasing the number of different models that can equally well explain the data. We have evaluated a method for the joint inversion of velocity and intrinsic attenuation using semiglobal inversion; this combines quantum particle-swarm optimization for the estimation of the intrinsic attenuation with nested gradient-descent iterations for the estimation of the P-wave velocity. This approach takes advantage of the fact that some physical properties, and in particular the intrinsic attenuation, can be represented using a reduced basis, substantially decreasing the dimension of the search space. We determine the feasibility of the method and its robustness to ambiguity with 2D synthetic examples. The 3D inversion of a field data set for a geologic medium with transversely isotropic anisotropy in velocity indicates the feasibility of the method for inverting large-scale real seismic data and improving the data fitting. The principal benefits of the semiglobal multiparameter inversion are the recovery of the intrinsic attenuation from the data and the recovery of the true undispersed infinite-frequency P-wave velocity, while mitigating ambiguity between the estimated parameters.

Application of sparse-layer inversion and harmonic bandwidth extension for a channel system in Southern Alberta, Canada

2020 ◽  
Vol 8 (2) ◽  
pp. T217-T229
Author(s):  
Yang Mu ◽  
John Castagna ◽  
Gabriel Gil
Keyword(s):  
Seismic Data ◽  
Reflection Coefficients ◽  
3D Seismic ◽  
Bandwidth Extension ◽  
Data Set ◽  
Frequency Spectra ◽  
Channel System ◽  
Southern Alberta ◽  
Reflectivity Inversion ◽  
3D Seismic Data

Sparse-layer reflectivity inversion decomposes a seismic trace into a limited number of simple layer responses and their corresponding reflection coefficients for top and base reflections. In contrast to sparse-spike inversion, the applied sparsity constraint is less biased against layer thickness and can thus better resolve thin subtuning layers. Application to a 3D seismic data set in Southern Alberta produces inverted impedances that have better temporal resolution and lateral stability and a less blocky appearance than sparse-spike inversion. Bandwidth extension harmonically extrapolated the frequency spectra of the inverted layers and nearly doubled the usable bandwidth. Although the prospective glauconitic sand tunes at approximately 37 m, bandwidth extension reduced the tuning thickness to 22 m. Bandwidth-extended data indicate a higher correlation with synthetic traces than the original seismic data and reveal features below the original tuning thickness. After bandwidth extension, the channel top and base are more evident on inline and crossline profiles. Lateral facies changes interpreted from the inverted acoustic impedance of the bandwidth-extended data are consistent with observations in wells.

Sparse reflectivity inversion for nonstationary seismic data

Geophysics ◽  
2014 ◽  
Vol 79 (3) ◽  
pp. V93-V105 ◽  
Author(s):  
Xintao Chai ◽  
Shangxu Wang ◽  
Sanyi Yuan ◽  
Jianguo Zhao ◽  
Langqiu Sun ◽  
...  
Keyword(s):  
Seismic Data ◽  
Inversion Method ◽  
The Novel ◽  
Field Surveys ◽  
Convolution Model ◽  
Stationary Signals ◽  
Text Filtering ◽  
Nonstationary Data ◽  
Attenuation And Dispersion ◽  
Reflectivity Inversion

Conventional reflectivity inversion methods are based on a stationary convolution model and theoretically require stationary seismic traces as input (i.e., those free of attenuation and dispersion effects). Reflectivity inversion for nonstationary data, which is typical for field surveys, requires us to first compensate for the earth’s [Formula: see text]-filtering effects by inverse [Formula: see text] filtering. However, the attenuation compensation for inverse [Formula: see text] filtering is inherently unstable, and offers no perfect solution. Thus, we presented a sparse reflectivity inversion method for nonstationary seismic data. We referred to this method as nonstationary sparse reflectivity inversion (NSRI); it makes the novel contribution of avoiding intrinsic instability associated with inverse [Formula: see text] filtering by integrating the earth’s [Formula: see text]-filtering operator into the stationary convolution model. NSRI also avoids time-variant wavelets that are typically required in time-variant deconvolution. Although NSRI is initially designed for nonstationary signals, it is suitable for stationary signals (i.e., using an infinite [Formula: see text]). The equations for NSRI only use reliable frequencies within the seismic bandwidth, and the basis pursuit optimizes a cost function of mixed [Formula: see text] norms to derive a stable and sparse solution. Synthetic examples show that NSRI can directly retrieve reflectivity from nonstationary data without advance inverse [Formula: see text] filtering. NSRI is satisfactorily stable in the presence of severe noise, and a slight error in the [Formula: see text] value does not greatly disturb the sensitivity of NSRI. A field data example confirmed the effectiveness of NSRI.

Amplitude and frequency anomalies in regional 3D seismic data surrounding the Mallik 5L-38 research site, Mackenzie Delta, Northwest Territories, Canada

Geophysics ◽  
2006 ◽  
Vol 71 (6) ◽  
pp. B183-B191 ◽  
Author(s):  
M. Riedel ◽  
G. Bellefleur ◽  
S. R. Dallimore ◽  
A. Taylor ◽  
J. F. Wright
Keyword(s):  
Northwest Territories ◽  
Gas Hydrate ◽  
Seismic Data ◽  
Ratio Method ◽  
Unfrozen Water ◽  
3D Seismic ◽  
Mackenzie Delta ◽  
Data Set ◽  
Seismic Amplitude ◽  
3D Seismic Data

Amplitude and frequency anomalies associated with lakes and drainage systems were observed in a 3D seismic data set acquired in the Mallik area, Mackenzie Delta, Northwest Territories, Canada. The site is characterized by large gas hydrate deposits inferred from well-log analyses and coring. Regional interpretation of the gas hydrate occurrences is mainly based on seismic amplitude anomalies, such as brightening or blanking of seismic energy. Thus, the scope of this research is to understand the nature of the amplitude behavior in the seismic data. We have therefore analyzed the 3D seismic data to define areas with amplitude reduction due to contamination from lakes and channels and to distinguish them from areas where amplitude blanking may be a geologic signal. We have used the spectral ratio method to define attenuation (Q) over different areas in the 3D volume and subsequently applied Q-compensation to attenuate lateral variations ofdispersive absorption. Underneath larger lakes, seismic amplitude is reduced and the frequency content is reduced to [Formula: see text], which is half the original bandwidth. Traces with source-receiver pairs located inside of lakes show an attenuation factor Q of [Formula: see text], approximately half of that obtained for source-receiver pairs situated on deep, continuous permafrost outside of lakes. Deeper reflections occasionally identified underneath lakes show low-velocity-related pull-down. The vertical extent of the washout zones is enhanced by acquisition with limited offsets and from processing parameters such as harsh mute functions to reduce noise from surface waves. The strong attenuation and seismic pull-down may indicate the presence of unfrozen water in deeper lakes and unfrozen pore water within the sediments underlying the lakes. Thus, the blanking underneath lakes is not necessarily related to gas migration or other in situ changes in physical properties potentially associated with the presence of gas hydrate.

Elastic impedance parameterization and inversion with Young’s modulus and Poisson’s ratio

Geophysics ◽  
2013 ◽  
Vol 78 (6) ◽  
pp. N35-N42 ◽  
Author(s):  
Zhaoyun Zong ◽  
Xingyao Yin ◽  
Guochen Wu
Keyword(s):  
Parameter Estimation ◽  
Young’S Modulus ◽  
Seismic Data ◽  
Poisson’S Ratio ◽  
Young's Modulus ◽  
Incident Angle ◽  
P Wave ◽  
Poisson's Ratio ◽  
Data Set ◽  
Elastic Impedance

Young’s modulus and Poisson’s ratio are related to quantitative reservoir properties such as porosity, rock strength, mineral and total organic carbon content, and they can be used to infer preferential drilling locations or sweet spots. Conventionally, they are computed and estimated with a rock physics law in terms of P-wave, S-wave impedances/velocities, and density which may be directly inverted with prestack seismic data. However, the density term imbedded in Young’s modulus is difficult to estimate because it is less sensitive to seismic-amplitude variations, and the indirect way can create more uncertainty for the estimation of Young’s modulus and Poisson’s ratio. This study combines the elastic impedance equation in terms of Young’s modulus and Poisson’s ratio and elastic impedance variation with incident angle inversion to produce a stable and direct way to estimate the Young’s modulus and Poisson’s ratio, with no need for density information from prestack seismic data. We initially derive a novel elastic impedance equation in terms of Young’s modulus and Poisson’s ratio. And then, to enhance the estimation stability, we develop the elastic impedance varying with incident angle inversion with damping singular value decomposition (EVA-DSVD) method to estimate the Young’s modulus and Poisson’s ratio. This method is implemented in a two-step inversion: Elastic impedance inversion and parameter estimation. The introduction of a model constraint and DSVD algorithm in parameter estimation renders the EVA-DSVD inversion more stable. Tests on synthetic data show that the Young’s modulus and Poisson’s ratio are still estimated reasonable with moderate noise. A test on a real data set shows that the estimated results are in good agreement with the results of well interpretation.

Joint inversion of P- and PS-waves in orthorhombic media: Theory and a physical modeling study

Geophysics ◽  
1999 ◽  
Vol 64 (1) ◽  
pp. 146-161 ◽  
Author(s):  
Vladimir Grechka ◽  
Stephen Theophanis ◽  
Ilya Tsvankin
Keyword(s):  
Shear Wave ◽  
Physical Modeling ◽  
Horizontal Plane ◽  
Joint Inversion ◽  
Symmetry Plane ◽  
P Wave ◽  
Simple Relationship ◽  
Converted Wave ◽  
Data Set ◽  
Converted Waves

Reflection traveltimes recorded over azimuthally anisotropic fractured media can provide valuable information for reservoir characterization. As recently shown by Grechka and Tsvankin, normal moveout (NMO) velocity of any pure (unconverted) mode depends on only three medium parameters and usually has an elliptical shape in the horizontal plane. Because of the limited information contained in the NMO ellipse of P-waves, it is advantageous to use moveout velocities of shear or converted modes in attempts to resolve the coefficients of realistic orthorhombic or lower‐symmetry fractured models. Joint inversion of P and PS traveltimes is especially attractive because it does not require shear‐wave excitation. Here, we show that for models composed of horizontal layers with a horizontal symmetry plane, the traveltime of converted waves is reciprocal with respect to the source and receiver positions (i.e., it remains the same if we interchange the source and receiver) and can be adequately described by NMO velocity on conventional‐length spreads. The azimuthal dependence of converted‐wave NMO velocity has the same form as for pure modes but requires the spatial derivatives of two-way traveltime for its determination. Using the generalized Dix equation of Grechka, Tsvankin, and Cohen, we derive a simple relationship between the NMO ellipses of pure and converted waves that provides a basis for obtaining shear‐wave information from P and PS data. For orthorhombic models, the combination of the reflection traveltimes of the P-wave and two split PS-waves makes it possible to reconstruct the azimuthally dependent NMO velocities of the pure shear modes and to find the anisotropic parameters that cannot be determined from P-wave data alone. The method is applied to a physical modeling data set acquired over a block of orthorhombic material—Phenolite XX-324. The inversion of conventional‐spread P and PS moveout data allowed us to obtain the orientation of the vertical symmetry planes and eight (out of nine) elastic parameters of the medium (the reflector depth was known). The remaining coefficient (c12 or δ(3) in Tsvankin’s notation) is found from the direct P-wave arrival in the horizontal plane. The inversion results accurately predict moveout curves of the pure S-waves and are in excellent agreement with direct measurements of the horizontal velocities.

scholarly journals Physical modeling of the Bazhenov formation in combination with CSEM and seismic methods

2021 ◽  
Vol 266 ◽  
pp. 07003
Author(s):  
V.V. Ananyev ◽  
G.S. Grigoryev ◽  
G.D. Gorelik
Keyword(s):  
Physical Model ◽  
Seismic Data ◽  
Physical Modeling ◽  
3D Seismic ◽  
Target Interval ◽  
Elastic Parameters ◽  
Bazhenov Formation ◽  
Testing Algorithms ◽  
Electrical Prospecting ◽  
3D Seismic Data

To predict the properties of the Bazhenov formation and to identify the areas in which oil deposits are localized the use of electrical prospecting methods is promising. The main goal of the current research is to evaluate the effectiveness of using Controlled Source Electro Magnetics (CSEM) exploration for the area forecast of the properties of the Bazhenov formation. The main objectives of physical modeling in this research are: Creation of an isotropic physical model corresponding to the electrical and elastic parameters of the section of the studied area, and performing 3D modeling on it with survey parameters that reproduce real seismic and CSEM surveys To convince that the contrast of the elastic properties and the ratio of the resistivity of the target interval of the Bazhenov formation and overlying rocks in the created physical model corresponds to the real petrophysical characteristics of the section of the studied area; Obtaining initial data for testing algorithms for synchronous inversion of seismic + electric and testing various approaches to processing and interpretation of CSEM and 3D seismic data.

Investigation of fluid effects on seismic responses through a physical modeling experiment

2020 ◽  
pp. 1-38
Author(s):  
Chao Xu ◽  
Pinbo Ding ◽  
Bangrang Di ◽  
Jianxin Wei
Keyword(s):  
Seismic Data ◽  
Physical Modeling ◽  
Seismic Velocity ◽  
Water Saturation ◽  
P Wave ◽  
Seismic Velocities ◽  
Seismic Responses ◽  
Modeling Experiment ◽  
Seismic Reflections ◽  
Tight Rocks

We investigated fluid effects on seismic responses using seismic data from a physical modeling experiment. Eight cubic samples with cavities quantitatively filled with air, oil, and water and sixteen non-fluid samples were set within a physical model. Both pre-stack and post-stack seismic responses of the samples were analyzed to quantitatively investigate the fluid effect on the seismic response. It was indicated that fluids could cause detectable changes in both pre-stack and post-stack seismic responses for tight rocks. At first, fluids filled within samples caused changes in pre-stack seismic responses. Visible differences could be detected between angle gathers of the samples filled with air, oil, and water. For the base reflections, the amplitudes at large angles of the air-filled and oil-filed samples are obviously stronger than those of the water-filled sample. In addition, the presence of fluids within samples led to significant changes in post-stack seismic reflections. For samples with similar P-wave impedances to the background, we found strong seismic reflections for the fluid samples and weak or even no reflections for the non-fluid samples. There was notable interference between the top and base reflections for the fluid samples while there was none for the non-fluid samples. Seismic velocities were estimated using the two-way travel times between the top and base reflections. The estimated seismic velocity gently declined with increasing water saturation until 90%. When the water saturation was more than 90%, the seismic velocity showed a steep increase.

A skeptic's view of VVAz and AVAz

2020 ◽  
Vol 39 (2) ◽  
pp. 128-134 ◽  
Author(s):  
Norbert Van De Coevering ◽  
Klaas Koster ◽  
Rob Holt
Keyword(s):  
Phase Ii ◽  
Seismic Data ◽  
Wave Mode ◽  
P Wave ◽  
Noise Levels ◽  
Reservoir Properties ◽  
Data Set ◽  
Lower Amplitude ◽  
Software Note ◽  
Dense Sampling

We have applied a modern amplitude- and azimuth-preserving seismic data processing workflow to the SEG Advanced Modeling Program (SEAM) Phase II Barrett classic data set — an orthorhombic synthetic seismic model that has extremely dense sampling of all azimuths and offsets. We analyze the resulting prestack depth-migrated offset vector tiles with a variety of methods and software. Note that we only analyze the P-P wave mode, which is the focus of our study. We demonstrate that observed azimuthal changes cannot be correlated with the model's reservoir properties. We have made the migrated data available through SEAM. Compared to modeled data, real onshore seismic data have significantly lower amplitude fidelity, higher noise levels, and more uncertainty in the migration velocity field used for processing. Since we are unable to relate the anisotropy measured from the fully sampled clean SEAM Phase II Barrett synthetic seismic data to the model's known anisotropy, we conclude that it is highly unlikely that azimuthal variations observed on real onshore seismic data will be predictive of reservoir fracture properties.

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