P-wave anisotropy from azimuthal AVO and velocity estimates using 3D seismic data from Saudi Arabia

Geophysics ◽  
2006 ◽  
Vol 71 (2) ◽  
pp. E7-E11 ◽  
Author(s):  
Ahmed M. Al-Marzoug ◽  
Fernando A. Neves ◽  
Jung J. Kim ◽  
Edgardo L. Nebrija
Keyword(s):  
Saudi Arabia ◽  
Seismic Data ◽  
Azimuthal Velocity ◽  
Fracture Pattern ◽  
P Wave ◽  
Velocity Analysis ◽  
Consistent Estimate ◽  
Amplitude Versus Offset ◽  
Gas Fields ◽  
East West

A horizontal well is most productive in tight reservoirs when it intersects a large number of vertical fractures, yet strata near the borehole remain mechanically stable. Azimuthal velocity analysis and P-wave amplitude versus offset (AVO) using 3D wide-azimuth prestack surface seismic data provide a remote yet detailed way to map a fracture pattern away from well control. We estimate fracture direction and relative fracture intensity from such data at two gas fields in Saudi Arabia. Our results show a small azimuthal variation in P-wave velocity (maximum 5%) and a larger variation in azimuthal AVO at the reservoir (larger than 100%). Computed fracture attributes for field 1 show a consistent east-west fracture direction. However, in field 2, fracture azimuth is variable but generally east-west and north-south, with the strongly anisotropic north-south orientation correlating with faults and areas of large structural curvature in the reservoir. In both fields, azimuthal AVO analysis shows a more consistent estimate of fracture orientation than velocity analysis. These estimates have been instrumental in planning prolific and safe horizontal wells

Fracture mapping from azimuthal velocity analysis using 3D surface seismic data

1996 ◽  
Author(s):  
Dennis Corrigan ◽  
Robert Withers ◽  
Jim Darnall ◽  
Tracey Skopinski
Keyword(s):  
Seismic Data ◽  
Azimuthal Velocity ◽  
Velocity Analysis ◽  
3D Surface ◽  
Fracture Mapping

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>

Fracture characterization of deep tight gas sands using azimuthal velocity and AVO seismic data in Saudi Arabia

2003 ◽  
Vol 22 (5) ◽  
pp. 469-475 ◽  
Author(s):  
Fernando A. Neves ◽  
Ahmed Al-Marzoug ◽  
Jung J. Kim ◽  
Ed L. Nebrija
Keyword(s):  
Saudi Arabia ◽  
Seismic Data ◽  
Azimuthal Velocity ◽  
Tight Gas ◽  
Fracture Characterization ◽  
Tight Gas Sands

Processing‐induced anisotropy

Geophysics ◽  
2002 ◽  
Vol 67 (6) ◽  
pp. 1920-1928 ◽  
Author(s):  
Vladimir Grechka ◽  
Ilya Tsvankin
Keyword(s):  
Seismic Data ◽  
Symmetry Axis ◽  
Homogeneous Medium ◽  
Transversely Isotropic ◽  
P Wave ◽  
Well Logs ◽  
Velocity Analysis ◽  
Induced Anisotropy ◽  
Vertical Heterogeneity ◽  
Vti Media

Processing of seismic data is often performed under the assumption that the velocity distribution in the subsurface can be approximated by a macromodel composed of isotropic homogeneous layers or blocks. Despite being physically unrealistic, such models are believed to be sufficient for describing the kinematics of reflection arrivals. In this paper, we examine the distortions in normal‐moveout (NMO) velocities caused by the intralayer vertical heterogeneity unaccounted for in velocity analysis. To match P‐wave moveout measurements from a horizontal or a dipping reflector overlaid by a vertically heterogeneous isotropic medium, the effective homogeneous overburden has to be anisotropic. This apparent anisotropy is caused not only by velocity monotonically increasing with depth, but also by random velocity variations similar to those routinely observed in well logs. Assuming that the effective homogeneous medium is transversely isotropic with a vertical symmetry axis (VTI), we express the VTI parameters through the actual depth‐dependent isotropic velocity function. If the reflector is horizontal, combining the NMO and vertical velocities always results in nonnegative values of Thomsen's coefficient δ. For a dipping reflector, the inversion of the P‐wave NMO ellipse yields a nonnegative Alkhalifah‐Tsvankin coefficient η that increases with dip. The values of η obtained by two other methods (2‐D dip‐moveout inversion and nonhyperbolic moveout analysis) are also nonnegative but generally differ from that needed to fit the NMO ellipse. For truly anisotropic (VTI) media, the influence of vertical heterogeneity above the reflector can lead to a bias toward positive δ and η estimates in velocity analysis.

P‐wave anisotropy from azimuthal velocity and AVO using wide‐azimuth 3D seismic data

2004 ◽  
Author(s):  
Ahmed M. Al‐Marzoug ◽  
Fernando A. Neves ◽  
Jung J. Kim ◽  
Edgardo L. Nebrija
Keyword(s):  
Seismic Data ◽  
Azimuthal Velocity ◽  
P Wave ◽  
3D Seismic ◽  
3D Seismic Data

An amplitude-based multiazimuthal approach to mapping fractures using P-wave 3D seismic data

Geophysics ◽  
2004 ◽  
Vol 69 (3) ◽  
pp. 690-698 ◽  
Author(s):  
Mu Luo ◽  
Brian J. Evans
Keyword(s):  
Physical Model ◽  
Seismic Data ◽  
Single Layer ◽  
P Wave ◽  
Data Sets ◽  
Test Results ◽  
Amplitude Versus Offset ◽  
Sorting Process ◽  
Seismic Reflections ◽  
3D Seismic Data

We have tested an amplitude-based multiazimuthal approach for mapping fractures which requires only a simple azimuth-offset sorting process. By displaying the amplitudes of all traces collected within a superbin, the method predicts fractures by mapping P-wave amplitude variations, in which a lineation within the map indicates the presence and the orientation of fractures within the superbin. Test results using physical model and field data sets suggest that the amplitude-based multiazimuthal approach could help to determine the presence of multiple fracture sets in a single layer, which may be expressed through subtle variations in P-wave multiazimuthal seismic reflections. Our experiments with a physical model containing manmade vertical fractures suggest that transmission effects could be one of the dominant factors which control azimuthal amplitude versus offset (AVO) behavior. The technique described in this paper can operate on any 3D P-wave seismic data with wide azimuth and offset distributions.

Seismic range equation

Geophysics ◽  
1991 ◽  
Vol 56 (7) ◽  
pp. 1015-1026 ◽  
Author(s):  
Richard E. Duren
Keyword(s):  
Seismic Data ◽  
Amplitude Spectrum ◽  
Data Gathering ◽  
P Wave ◽  
Target Strength ◽  
Load Impedance ◽  
Transmission Coefficients ◽  
Amplitude Versus Offset ◽  
Well Data ◽  
Range Equation

The seismic range equation is the seismic equivalent to the radar range equation. It unites the relevant factors in a marine data gathering system (source, subsurface, target, and receiving system) into a single formulation and provides a systematic approach to seismic data analysis, which we have used to improve seismic data processing for marine basins around the world. While most of the concepts contained in the seismic range equation are well known, this is the first time all have been put together, described in such detail, and used effectively (by way of modeling) to improve processing. A wavelet’s amplitude spectrum can be calculated using the seismic range equation, and its phase spectrum can be calculated by considering the phase contributions from source to receiver. An amplitude‐versus‐offset (AVO) example supports our approach. Source array geometry and the outgoing waveforms from the array elements are required. The receiving system’s load impedance and the hydrophone array’s geometry, sensitivity, and impedance are also required. Subsurface factors and target strength can be determined by assuming a horizontally layered subsurface and ray theory. The required layer thicknesses, P‐wave velocities, and densities can be generated by hand, statistically, or from well data. Well data are not required at the location of interest. The Zoeppritz equations furnish all P-wave reflection and transmission coefficients along a raypath. Seismic data at the location of interest can be used to estimate an attenuation constant (effective Q).

Fracture Mapping Using Azimuthal Velocity/AVOA Seismic Data in Saudi Arabia

2003 ◽  
Author(s):  
F.A. Neves ◽  
A. Al-Marzoug ◽  
J.J. Kim ◽  
E. Nebrija
Keyword(s):  
Saudi Arabia ◽  
Seismic Data ◽  
Azimuthal Velocity ◽  
Fracture Mapping

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.

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