Shear‐wave velocity and density estimation from PS-wave AVO analysis: Application to an OBS dataset from the North Sea

Geophysics ◽  
2000 ◽  
Vol 65 (5) ◽  
pp. 1446-1454 ◽  
Author(s):  
Side Jin ◽  
G. Cambois ◽  
C. Vuillermoz
Keyword(s):  
Wave Velocity ◽  
North Sea ◽  
Random Noise ◽  
P Wave ◽  
S Wave ◽  
Data Set ◽  
Avo Analysis ◽  
The North ◽  
Avo Inversion ◽  
The North Sea

S-wave velocity and density information is crucial for hydrocarbon detection, because they help in the discrimination of pore filling fluids. Unfortunately, these two parameters cannot be accurately resolved from conventional P-wave marine data. Recent developments in ocean‐bottom seismic (OBS) technology make it possible to acquire high quality S-wave data in marine environments. The use of (S)-waves for amplitude variation with offset (AVO) analysis can give better estimates of S-wave velocity and density contrasts. Like P-wave AVO, S-wave AVO is sensitive to various types of noise. We investigate numerically and analytically the sensitivity of AVO inversion to random noise and errors in angles of incidence. Synthetic examples show that random noise and angle errors can strongly bias the parameter estimation. The use of singular value decomposition offers a simple stabilization scheme to solve for the elastic parameters. The AVO inversion is applied to an OBS data set from the North Sea. Special prestack processing techniques are required for the success of S-wave AVO inversion. The derived S-wave velocity and density contrasts help in detecting the fluid contacts and delineating the extent of the reservoir sand.

Bayesian closed-skew Gaussian inversion of seismic AVO data for elastic material properties

Geophysics ◽  
2010 ◽  
Vol 75 (1) ◽  
pp. R1-R11 ◽  
Author(s):  
Omid Karimi ◽  
Henning Omre ◽  
Mohsen Mohammadzadeh
Keyword(s):  
Elastic Material ◽  
Material Properties ◽  
P Wave ◽  
S Wave ◽  
High Dimensions ◽  
Seismic Amplitude ◽  
The North ◽  
Avo Inversion ◽  
Amplitude Versus Offset ◽  
The North Sea

Bayesian closed-skew Gaussian inversion is defined as a generalization of traditional Bayesian Gaussian inversion, which is used frequently in seismic amplitude-versus-offset (AVO) inversion. The new model captures skewness in the variables of interest; hence, the posterior model for log-transformed elastic material properties given seismic AVO data might be a skew probability density function. The model is analytically tractable, and this makes it applicable in high-dimensional 3D inversion problems. Assessment of the posterior models in high dimensions requires numerical approximations, however. The Bayesian closed-skew Gaussian inversion approach has been applied on real elastic material properties from a well in the Sleipner field in the North Sea. A comparison with results from traditional Bayesian Gaussian inversion shows that the mean square error of predictions of P-wave and S-wave velocities are reduced by a factor of two, although somewhat less for density predictions.

The impact of common‐offset migration on porosity estimation by AVO inversion

Geophysics ◽  
2001 ◽  
Vol 66 (3) ◽  
pp. 755-762 ◽  
Author(s):  
Arild Buland ◽  
Martin Landrø
Keyword(s):  
P Wave ◽  
High Porosity ◽  
S Wave ◽  
Seismic Line ◽  
Data Set ◽  
The North ◽  
Avo Inversion ◽  
Time Migration ◽  
The Impact ◽  
Porosity Estimation

The impact of prestack time migration on porosity estimation has been tested on a 2-D seismic line from the Valhall/Hod area in the North Sea. Porosity is estimated in the Cretaceous chalk section in a two‐step procedure. First, P-wave and S-wave velocity and density are estimated by amplitude variation with offset (AVO) inversion. These parameters are then linked to porosity through a petrophysical rock data base based on core plug analysis. The porosity is estimated both from unmigrated and prestack migrated seismic data. For the migrated data set, a standard prestack Kirchhoff time migration is used, followed by simple angle and amplitude corrections. Compared to modern high‐cost, true amplitude migration methods, this approach is faster and more practical. The test line is structurally fairly simple, with a maximum dip of 5°; but the results differ significantly, depending on whether migration is applied prior to the inversion. The maximum difference in estimated porosity is of the order of 10% (about 50% relative change). High‐porosity zones estimated from the unmigrated data were not present on the porosity section estimated from the migrated data.

Bayesian linearized AVO inversion

Geophysics ◽  
2003 ◽  
Vol 68 (1) ◽  
pp. 185-198 ◽  
Author(s):  
Arild Buland ◽  
Henning Omre
Keyword(s):  
Wave Velocity ◽  
Posterior Distribution ◽  
Acoustic Impedance ◽  
Velocity Ratio ◽  
P Wave ◽  
Inversion Method ◽  
Inversion Algorithm ◽  
S Wave ◽  
Data Set ◽  
Avo Inversion

A new linearized AVO inversion technique is developed in a Bayesian framework. The objective is to obtain posterior distributions for P‐wave velocity, S‐wave velocity, and density. Distributions for other elastic parameters can also be assessed—for example, acoustic impedance, shear impedance, and P‐wave to S‐wave velocity ratio. The inversion algorithm is based on the convolutional model and a linearized weak contrast approximation of the Zoeppritz equation. The solution is represented by a Gaussian posterior distribution with explicit expressions for the posterior expectation and covariance; hence, exact prediction intervals for the inverted parameters can be computed under the specified model. The explicit analytical form of the posterior distribution provides a computationally fast inversion method. Tests on synthetic data show that all inverted parameters were almost perfectly retrieved when the noise approached zero. With realistic noise levels, acoustic impedance was the best determined parameter, while the inversion provided practically no information about the density. The inversion algorithm has also been tested on a real 3‐D data set from the Sleipner field. The results show good agreement with well logs, but the uncertainty is high.

Data-driven adaptive decomposition of multicomponent seabed seismic recordings: Application to shallow-water data from the North Sea

Geophysics ◽  
2007 ◽  
Vol 72 (6) ◽  
pp. V133-V142 ◽  
Author(s):  
Remco Muijs ◽  
Johan O. A. Robertsson ◽  
Klaus Holliger
Keyword(s):  
North Sea ◽  
Decomposition Method ◽  
Data Driven ◽  
Full Potential ◽  
S Wave ◽  
Data Set ◽  
The North ◽  
Decomposition Procedure ◽  
The North Sea

Exploiting the full potential of multicomponent seabed seismic recordings requires the decomposition of the recorded data into their upgoing and downgoing P- and S-wave constituents. We present a case study from the North Sea, where a novel adaptive wave-equation-based decomposition method is applied to a 2D data set shot inline with a cable-based seabed seismic acquisition system. The data were recorded in relatively shallow [Formula: see text] water, such that severe interference exists between primary reflections and water-layer multiples. Such conditions represent a challenge for many decomposition methods, because these often require a significant amount of interpretive, user-defined input. Conversely, the adaptive algorithm demonstrated in this study is fully data-driven, requiring as sole input a rough estimate of the water depth. The importance of careful mutual calibration of the sensors is demonstrated by critically assessing the properties of the derived calibration filters and the resulting estimates of the elastic properties of the seabed. To assess the effectiveness of the decomposition procedure, we compare a number of key events identified in the unprocessed data with their equivalents in the decomposed wavefields. The results of this case study show that the noninteractive decomposition method, which was demonstrated on seabed seismic data acquired in deep [Formula: see text] water, can be applied successfully in shallower conditions without further modification.

High-resolution three-term AVO inversion by means of a Trivariate Cauchy probability distribution

Geophysics ◽  
2011 ◽  
Vol 76 (3) ◽  
pp. R43-R55 ◽  
Author(s):  
Wubshet Alemie ◽  
Mauricio D. Sacchi
Keyword(s):  
High Resolution ◽  
Wave Velocity ◽  
P Wave ◽  
S Wave ◽  
Avo Inversion ◽  
P Wave Velocity ◽  
Multivariate Gaussian ◽  
Density Perturbations ◽  
Gaussian Prior ◽  
Scale Matrix

Three-term AVO inversion can be used to estimate P-wave velocity, S-wave velocity, and density perturbations from reflection seismic data. The density term, however, exhibits little sensitivity to amplitudes and, therefore, its inversion is unstable. One way to stabilize the density term is by including a scale matrix that provides correlation information between the three unknown AVO parameters. We investigate a Bayesian procedure to include sparsity and a scale matrix in the three-term AVO inversion problem. To this end, we model the prior distribution of the AVO parameters via a Trivariate Cauchy distribution. We found an iterative algorithm to solve the Bayesian inversion and, in addition, comparisons are provided with the classical inversion approach that uses a Multivariate Gaussian prior. It is important to point out that the Multivariate Gaussian prior allows us to include the correlation of the AVO parameters in the solution of the inverse problem. The Trivariate Cauchy prior not only permits us to incorporate correlation but also leads to high-resolution (broadband) P-wave velocity, S-wave velocity, and density perturbations.

Amplitude variation with offset inversion using the reflectivity method

Geophysics ◽  
2016 ◽  
Vol 81 (4) ◽  
pp. R185-R195 ◽  
Author(s):  
Hongxing Liu ◽  
Jingye Li ◽  
Xiaohong Chen ◽  
Bo Hou ◽  
Li Chen
Keyword(s):  
Wave Velocity ◽  
P Wave ◽  
Transmission Losses ◽  
Amplitude Variation ◽  
Data Set ◽  
Amplitude Variation With Offset ◽  
Avo Inversion ◽  
Propagation Effects ◽  
Reflectivity Method ◽  
Internal Multiples

Most existing amplitude variation with offset (AVO) inversion methods are based on the Zoeppritz’s equation or its approximations. These methods assume that the amplitude of seismic data depends only on the reflection coefficients, which means that the wave-propagation effects, such as geometric spreading, attenuation, transmission loss, and multiples, have been fully corrected or attenuated before inversion. However, these requirements are very strict and can hardly be satisfied. Under a 1D assumption, reflectivity-method-based inversions are able to handle transmission losses and internal multiples. Applications of these inversions, however, are still time-consuming and complex in computation of differential seismograms. We have evaluated an inversion methodology based on the vectorized reflectivity method, in which the differential seismograms can be calculated from analytical expressions. It is computationally efficient. A modification is implemented to transform the inversion from the intercept time and ray-parameter domain to the angle-gather domain. AVO inversion is always an ill-posed problem. Following a Bayesian approach, the inversion is stabilized by including the correlation of the P-wave velocity, S-wave velocity, and density. Comparing reflectivity-method-based inversion with Zoeppritz-based inversion on a synthetic data and a real data set, we have concluded that reflectivity-method-based inversion is more accurate when the propagation effects of transmission losses and internal multiples are not corrected. Model testing has revealed that the method is robust at high noise levels.

DISPERSANT EFFECTIVENESS IN FIELD TRIALS AND IN OPERATIONAL RESPONSE

1997 ◽  
Vol 1997 (1) ◽  
pp. 923-923
Author(s):  
Tim Lunel ◽  
Peter Wood ◽  
Louise Davies
Keyword(s):  
North Sea ◽  
Quantitative Data ◽  
Field Tests ◽  
Field Trials ◽  
Data Set ◽  
Oil Droplet ◽  
The North ◽  
The North Sea ◽  
Dispersant Effectiveness ◽  
Oil Droplet Size

ABSTRACT The North Sea field tests described in the paper have provided a quantitative data set on dispersant efficiency that can be used to calibrate laboratory dispersant tests. Comparisons of efficiency figures from the EXDET, IFP, Swirling Flask, and WSL tests with the field dispersant efficiency figures indicate that the WSL test comes closest to replicating the observed dispersion, in terms of both the percentage of oil dispersed and the oil droplet size of the dispersion. This paper, with the accompanying presentation in the Sea Empress session of the conference, demonstrates that a combination of quantitative field tests and the WSL test can be used to guide responders in decisions of whether to use dispersants in response to an oil spill. The WSL test and the field trials indicated that dispersants were likely to be effective against both the Forties Blend crude oil and the weathered oil. These predictions were confirmed by the successful dispersant operation at the Sea Empress incident.

Fracture detection using azimuthal variation of P-wave moveout from orthogonal seismic survey lines

Geophysics ◽  
1999 ◽  
Vol 64 (4) ◽  
pp. 1193-1201 ◽  
Author(s):  
Xiang‐Yang Li
Keyword(s):  
P Wave ◽  
Seismic Survey ◽  
Fracture Detection ◽  
Data Set ◽  
The North ◽  
High Impedance ◽  
Impedance Contrast ◽  
The North Sea ◽  
Marine Exploration ◽  
Orthogonal Pairs

An algorithm is proposed for determining the fracture orientation based on the azimuthal variations in the P-wave reflection moveout for a target interval. The differential moveout between orthogonal survey lines from the bottom of a given target shows cos 2ϕ variations with the line azimuth ϕ measured from the fracture strike for a fixed offset. A configuration of four intersecting survey lines may be used to quantify the fracture strike. The four lines form two orthogonal pairs, and the fracture strike can be obtained by analyzing the crossplot of the two corresponding pairs of the differential moveouts. An offset‐depth ratio (x/z) of 1.0 or greater (up to 1.5) is often required to quantify the moveout difference reliably. The sensitivity of the method is further enhanced by low/high impedance contrast at the top target interface but is greatly reduced by high/low impedance contrast. The method may be particularly useful in marine exploration with repeated surveys of various vintages where continuous azimuthal coverage is often not available. A data set from the North Sea is used to illustrate the technique.

Useful approximations for converted‐wave AVO

Geophysics ◽  
2001 ◽  
Vol 66 (6) ◽  
pp. 1721-1734 ◽  
Author(s):  
Antonio C. B. Ramos ◽  
John P. Castagna
Keyword(s):  
Wave Velocity ◽  
Random Noise ◽  
P Wave ◽  
Rock Properties ◽  
Negative Slope ◽  
High Contrast ◽  
Fractional Change ◽  
S Wave ◽  
Converted Wave ◽  
Low Contrast

Converted‐wave amplitude versus offset (AVO) behavior may be fit with a cubic relationship between reflection coefficient and ray parameter. Attributes extracted using this form can be directly related to elastic parameters with low‐contrast or high‐contrast approximations to the Zoeppritz equations. The high‐contrast approximation has the advantage of greater accuracy; the low‐contrast approximation is analytically simpler. The two coefficients of the low‐contrast approximation are a function of the average ratio of compressional‐to‐shear‐wave velocity (α/β) and the fractional changes in S‐wave velocity and density (Δβ/β and Δρ/ρ). Because of its simplicity, the low‐contrast approximation is subject to errors, particularly for large positive contrasts in P‐wave velocity associated with negative contrasts in S‐wave velocity. However, for incidence angles up to 40° and models confined to |Δβ/β| < 0.25, the errors in both coefficients are relatively small. Converted‐wave AVO crossplotting of the coefficients of the low‐contrast approximation is a useful interpretation technique. The background trend in this case has a negative slope and an intercept proportional to the α/β ratio and the fractional change in S‐wave velocity. For constant α/β ratio, an attribute trace formed by the weighted sum of the coefficients of the low‐contrast approximation provides useful estimates of the fractional change in S‐wave velocity and density. Using synthetic examples, we investigate the sensitivity of these parameters to random noise. Integrated P‐wave and converted‐wave analysis may improve estimation of rock properties by combining extracted attributes to yield fractional contrasts in P‐wave and S‐wave velocities and density. Together, these parameters may provide improved direct hydrocarbon indication and can potentially be used to identify anomalies caused by low gas saturations.

Full-waveform matching of VP and VS models from surface waves

2019 ◽  
Vol 218 (3) ◽  
pp. 1873-1891 ◽  
Author(s):  
Farbod Khosro Anjom ◽  
Daniela Teodor ◽  
Cesare Comina ◽  
Romain Brossier ◽  
Jean Virieux ◽  
...  
Keyword(s):  
Surface Waves ◽  
Wave Velocity ◽  
Real Data ◽  
Test Site ◽  
P Wave ◽  
S Wave ◽  
Surface Wave Dispersion ◽  
Data Set ◽  
Near Surface ◽  
Velocity Models

SUMMARY The analysis of surface wave dispersion curves (DCs) is widely used for near-surface S-wave velocity (VS) reconstruction. However, a comprehensive characterization of the near-surface requires also the estimation of P-wave velocity (VP). We focus on the estimation of both VS and VP models from surface waves using a direct data transform approach. We estimate a relationship between the wavelength of the fundamental mode of surface waves and the investigation depth and we use it to directly transform the DCs into VS and VP models in laterally varying sites. We apply the workflow to a real data set acquired on a known test site. The accuracy of such reconstruction is validated by a waveform comparison between field data and synthetic data obtained by performing elastic numerical simulations on the estimated VP and VS models. The uncertainties on the estimated velocity models are also computed.

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