Kirchoff Migration and Elastic P-Wave AVO Analysis Corrected for a Thinly Layered Overburden

1995 ◽  
Author(s):  
M. Widmaier ◽  
T. Müller ◽  
S. Shapiro ◽  
P. Hubral
Keyword(s):  
P Wave ◽  
Avo Analysis

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.

scholarly journals New AVO Attributes and Their Applications for Facies and Hydrocarbon Prediction: A Case Study from the Northern Malay Basin

2020 ◽  
Vol 10 (21) ◽  
pp. 7786
Author(s):  
Tsara Kamilah Ridwan ◽  
Maman Hermana ◽  
Luluan Almanna Lubis ◽  
Zaky Ahmad Riyadi
Keyword(s):  
Quality Factor ◽  
Poisson’S Ratio ◽  
Water Saturation ◽  
P Wave ◽  
Poisson's Ratio ◽  
Fluid Distribution ◽  
Well Log ◽  
Log Data ◽  
Avo Analysis ◽  
Avo Attributes

Amplitude versus offset (AVO) analysis integration to well log analysis is considered one of the advanced techniques to improve the understanding of facies and fluid analysis. Generating AVO attributes are one solution to give an accurate result in facies and fluid characterization. This study is focused on a field of Northern Malay basin, which is associated with a fluvial-deltaic environment, where this system has high heterogeneity, whether it is vertically or horizontally. This research is aimed to demonstrate an application of the scale of quality factor of P-wave (SQp) and the scale of quality factor of S-wave (SQs) AVO attributes for facies and fluid types separation in field scale. These methods are supposed to be more sensitive to predict the hydrocarbons and give less ambiguity. SQp and SQs are the new AVO attributes, which derived from AVO analysis and created according to the intercept product (A) and gradient (B). These new attributes have also been compared to the common method, which is the Scaled Poisson’s Ratio attribute. By comparing with the Scaled Poisson’s Ratio attribute, SQp and SQs attributes are more accurate in determining facies and hydrocarbon. SQp and SQs AVO attributes are integrated with well log data and considered as the best technique to determine facies and fluid distribution. They are interpreted by using angle-stack seismic data based on amplitude contrast on interfaces. Well log data, e.g., density and sonic logs, are used to generate synthetic seismogram and well tie requirements. The volume of shale, volume of coal, porosity, and water saturation logs are used to identify facies and fluid in well log scale. This analysis includes AVO gradient analysis and AVO cross plot to identify the fluid class. Gassmann’s fluid substitution modeling is also generated in the well logs and AVO synthetics for in situ, pure brine, and pure gas cases. The application of the SQp and SQs attributes successfully interpreted facies and fluids distributions in the Northern Malay Basin.

Simultaneous inversion for model geometry and elastic parameters

Geophysics ◽  
1999 ◽  
Vol 64 (1) ◽  
pp. 182-190 ◽  
Author(s):  
Yanghua Wang
Keyword(s):  
Joint Inversion ◽  
Real Data ◽  
Structural Effect ◽  
P Wave ◽  
Inversion Method ◽  
S Wave ◽  
Elastic Parameters ◽  
Simultaneous Inversion ◽  
Avo Analysis ◽  
The North

Both traveltimes and amplitudes in reflection seismology are used jointly in an inversion to simultaneously invert for the interface geometry and the elastic parameters at the reflectors. The inverse problem has different physical dimensions in both data and model spaces. Practical approaches are proposed to tackle the dimensional difficulties. In using the joint inversion, which may properly take care of the structural effect, one potentially improves the estimates of the subsurface elastic parameters in the traditional analysis of amplitude variation with offset (AVO). Analysis of the elastic parameters estimated, using the ratio of s-wave to P-wave velocity contrasts and the deviation of this parameter from a normal background trend, promises to have application in AVO analysis. The inversion method is demonstrated by application to real data from the North Sea.

Fracture characterization integrating volumetric geometric attributes and P-wave Azimuthal AVO analysis

2012 ◽  
Author(s):  
Zhaoming Wang ◽  
Duoming Zheng ◽  
Lijuan Zhang ◽  
Feng Shen ◽  
Yang Zhang
Keyword(s):  
P Wave ◽  
Fracture Characterization ◽  
Avo Analysis

Approximations to the Zoeppritz equations and their use in AVO analysis

Geophysics ◽  
1999 ◽  
Vol 64 (6) ◽  
pp. 1920-1927 ◽  
Author(s):  
Yanghua Wang
Keyword(s):  
Linear Approximation ◽  
Wave Velocity ◽  
Order Approximation ◽  
Quadratic Function ◽  
P Wave ◽  
S Wave ◽  
Elastic Parameters ◽  
Amplitude Variation With Offset ◽  
Zoeppritz Equations ◽  
Avo Analysis

To efficiently invert seismic amplitudes for elastic parameters, pseudoquartic approximations to the Zoeppritz equations are derived to calculate P-P-wave reflection and transmission coefficients as a function of the ray parameter p. These explicit expressions have a compact form in which the coefficients of the p2 and p4 terms are given in terms of the vertical slownesses. The amplitude coefficients are also represented as a quadratic function of the elastic contrasts at an interface and are compared to the linear approximation used in conventional amplitude variation with offset (AVO) analysis, which can invert for only two elastic parameters. Numerical analysis with the second‐order approximation shows that the condition number of the Fréchet matrix for three elastic parameters is improved significantly from using a linear approximation. Therefore, those quadratic approximations can be used directly with amplitude information to estimate not only two but three parameters: P-wave velocity contrast, S-wave velocity contrast, and the ratio of S-wave and P-wave velocities at an interface.

scholarly journals Three-component amplitude versus offset analysis

1989 ◽  
Vol 20 (2) ◽  
pp. 257
Author(s):  
D.R. Miles ◽  
G. Gassaway ◽  
L. Bennett ◽  
R. Brown
Keyword(s):  
Seismic Data ◽  
P Wave ◽  
S Wave ◽  
Converted Wave ◽  
P Waves ◽  
S Waves ◽  
Avo Analysis ◽  
Avo Inversion ◽  
Amplitude Versus Offset ◽  
Converted Waves

Three-component (3-C) amplitude versus offset (AVO) inversion is the AVO analysis of the three major energies in the seismic data, P-waves, S-waves and converted waves. For each type of energy the reflection coefficients at the boundary are a function of the contrast across the boundary in velocity, density and Poisson's ratio, and of the angle of incidence of the incoming wave. 3-C AVO analysis exploits these relationships to analyse the AVO changes in the P, S, and converted waves. 3-C AVO analysis is generally done on P, S, and converted wave data collected from a single source on 3-C geophones. Since most seismic sources generate both P and S-waves, it follows that most 3-C seismic data may be used in 3-C AVO inversion. Processing of the P-wave, S-wave and converted wave gathers is nearly the same as for single-component P-wave gathers. In split-spread shooting, the P-wave and S-wave energy on the radial component is one polarity on the forward shot and the opposite polarity on the back shot. Therefore to use both sides of the shot, the back shot must be rotated 180 degrees before it can be stacked with the forward shot. The amplitude of the returning energy is a function of all three components, not just the vertical or radial, so all three components must be stacked for P-waves, then for S-waves, and finally for converted waves. After the gathers are processed, reflectors are picked and the amplitudes are corrected for free-surface effects, spherical divergence and the shot and geophone array geometries. Next the P and S-wave interval velocities are calculated from the P and S-wave moveouts. Then the amplitude response of the P and S-wave reflections are analysed to give Poisson's ratio. The two solutions are then compared and adjusted until they match each other and the data. Three-component AVO inversion not only yields information about the lithologies and pore-fluids at a specific location; it also provides the interpreter with good correlations between the P-waves and the S-waves, and between the P and converted waves, thus greatly expanding the value of 3-C seismic data.

3-D AVO analysis and modeling applied to fracture detection in coalbed methane reservoirs

Geophysics ◽  
1997 ◽  
Vol 62 (6) ◽  
pp. 1683-1695 ◽  
Author(s):  
Antonio C. B. Ramos ◽  
Thomas L. Davis
Keyword(s):  
Poisson’S Ratio ◽  
Coalbed Methane ◽  
Spatial Location ◽  
Azimuthal Anisotropy ◽  
P Wave ◽  
Poisson's Ratio ◽  
Seismic Survey ◽  
Data Set ◽  
Avo Analysis ◽  
Coalbed Methane Reservoir

Over the years, amplitude variation with‐offset (AVO) analysis has been used successfully to predict reservoir properties and fluid contents, in some cases allowing the spatial location of gas‐water and gas‐oil contacts. In this paper, we show that a 3-D AVO technique also can be used to characterize fractured reservoirs, allowing spatial location of crack density variations. The Cedar Hill Field in the San Juan Basin, New Mexico, produces methane from the fractured coalbeds of the Fruitland Formation. The presence of fracturing is critical to methane production because of the absence of matrix permeability in the coals. To help characterize this coalbed reservoir, a 3-D, multicomponent seismic survey was acquired in this field. In this study, prestack P‐wave amplitude data from the multicomponent data set are used to delineate zones of large Poisson's ratio contrasts (or high crack densities) in the coalbed methane reservoir, while source‐receiver azimuth sorting is used to detect preferential directions of azimuthal anisotropy caused by the fracturing system of coal. Two modeling techniques (using ray tracing and reflectivity methods) predict the effects of fractured coal‐seam zones on angle‐dependent P‐wave reflectivity. Synthetic common‐midpoint (CMP) gathers are generated for a horizontally layered earth model that uses elastic parameters derived from sonic and density log measurements. Fracture density variations in coalbeds are simulated by anisotropic modeling. The large acoustic impedance contrasts associated with the sandstone‐coal interfaces dominate the P‐wave reflectivity response. They far outweigh the effects of contrasts in anisotropic parameters for the computed models. Seismic AVO analysis of nine macrobins obtained from the 3-D volume confirms model predictions. Areas with large AVO intercepts indicate low‐velocity coals, possibly related to zones of stress relief. Areas with large AVO gradients identify coal zones of large Poisson's ratio contrasts and therefore high fracture densities in the coalbed methane reservoir. The 3-D AVO product and Poisson's variation maps combine these responses, producing a picture of the reservoir that includes its degree of fracturing and its possible stress condition. Source‐receiver azimuth sorting is used to detect preferential directions of azimuthal anisotropy caused by the fracturing system of coal.

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