EXTRACTING SUB-SURFACE INFORMATION FROM LONG OFFSET P-WAVE DATA—A CHEAPER ALTERNATIVE TO MULTICOMPONENT OBC FOR EXPLORATION?

2002 ◽  
Vol 42 (1) ◽  
pp. 627
Author(s):  
R.G. Williams ◽  
G. Roberts ◽  
K. Hawkins
Keyword(s):  
Seismic Energy ◽  
Higher Order ◽  
P Wave ◽  
S Wave ◽  
Additional Information ◽  
Wave Data ◽  
Wave Velocities ◽  
Avo Inversion ◽  
Long Offset ◽  
Multicomponent Data

Seismic energy that has been mode converted from pwave to s-wave in the sub-surface may be recorded by multi-component surveys to obtain information about the elastic properties of the earth. Since the energy converted to s-wave is missing from the p-wave an alternative to recording OBC multi-component data is to examine p-wave data for the missing energy. Since pwave velocities are generally faster than s-wave velocities, then for a given reflection point the converted s-wave signal reaches the surface at a shorter offset than the equivalent p-wave information. Thus, it is necessary to record longer offsets for p-wave data than for multicomponent data in order to measure the same information.A non-linear, wide-angle (including post critical) AVO inversion has been developed that allows relative changes in p-wave velocities, s-wave velocities and density to be extracted from long offset p-wave data. To extract amplitudes at long offsets for this inversion it is necessary to image the data correctly, including correcting for higher order moveout and possibly anisotropy if it is present.The higher order moveout may itself be inverted to yield additional information about the anisotropy of the sub-surface.

Estimating shear‐wave velocities from P-wave and converted‐wave data

Geophysics ◽  
1999 ◽  
Vol 64 (2) ◽  
pp. 504-507 ◽  
Author(s):  
Franklyn K. Levin
Keyword(s):  
Wave Velocity ◽  
Seismic Data ◽  
P Wave ◽  
S Wave ◽  
Converted Wave ◽  
Wave Data ◽  
Wave Velocities ◽  
Shear Wave Velocities ◽  
P Wave Velocity ◽  
Growing Body

Tessmer and Behle (1988) show that S-wave velocity can be estimated from surface seismic data if both normal P-wave data and converted‐wave data (P-SV) are available. The relation of Tessmer and Behle is [Formula: see text] (1) where [Formula: see text] is the S-wave velocity, [Formula: see text] is the P-wave velocity, and [Formula: see text] is the converted‐wave velocity. The growing body of converted‐wave data suggest a brief examination of the validity of equation (1) for velocities that vary with depth.

Direct shear-wave seismic survey in Sanhu area, Qaidam Basin, west China

2022 ◽  
Vol 41 (1) ◽  
pp. 47-53
Author(s):  
Zhiwen Deng ◽  
Rui Zhang ◽  
Liang Gou ◽  
Shaohua Zhang ◽  
Yuanyuan Yue ◽  
...  
Keyword(s):  
Shear Wave ◽  
Reservoir Characterization ◽  
Qaidam Basin ◽  
P Wave ◽  
Data Sets ◽  
Direct Shear ◽  
Subsurface Structure ◽  
S Wave ◽  
West China ◽  
Wave Data

The formation containing shallow gas clouds poses a major challenge for conventional P-wave seismic surveys in the Sanhu area, Qaidam Basin, west China, as it dramatically attenuates seismic P-waves, resulting in high uncertainty in the subsurface structure and complexity in reservoir characterization. To address this issue, we proposed a workflow of direct shear-wave seismic (S-S) surveys. This is because the shear wave is not significantly affected by the pore fluid. Our workflow includes acquisition, processing, and interpretation in calibration with conventional P-wave seismic data to obtain improved subsurface structure images and reservoir characterization. To procure a good S-wave seismic image, several key techniques were applied: (1) a newly developed S-wave vibrator, one of the most powerful such vibrators in the world, was used to send a strong S-wave into the subsurface; (2) the acquired 9C S-S data sets initially were rotated into SH-SH and SV-SV components and subsequently were rotated into fast and slow S-wave components; and (3) a surface-wave inversion technique was applied to obtain the near-surface shear-wave velocity, used for static correction. As expected, the S-wave data were not affected by the gas clouds. This allowed us to map the subsurface structures with stronger confidence than with the P-wave data. Such S-wave data materialize into similar frequency spectra as P-wave data with a better signal-to-noise ratio. Seismic attributes were also applied to the S-wave data sets. This resulted in clearly visible geologic features that were invisible in the P-wave data.

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.

Shallow velocity structure and poisson's ratio at the Tarzana, California, strong-motion accelerometer site

1996 ◽  
Vol 86 (6) ◽  
pp. 1704-1713 ◽  
Author(s):  
R. D. Catchings ◽  
W. H. K. Lee
Keyword(s):  
Urban Areas ◽  
Velocity Structure ◽  
Strong Motion ◽  
P Wave ◽  
S Wave ◽  
Near Surface ◽  
Velocity Models ◽  
Wave Velocities ◽  
Poisson's Ratios ◽  
Poisson’S Ratios

Abstract The 17 January 1994, Northridge, California, earthquake produced strong ground shaking at the Cedar Hills Nursery (referred to here as the Tarzana site) within the city of Tarzana, California, approximately 6 km from the epicenter of the mainshock. Although the Tarzana site is on a hill and is a rock site, accelerations of approximately 1.78 g horizontally and 1.2 g vertically at the Tarzana site are among the highest ever instrumentally recorded for an earthquake. To investigate possible site effects at the Tarzana site, we used explosive-source seismic refraction data to determine the shallow (<70 m) P-and S-wave velocity structure. Our seismic velocity models for the Tarzana site indicate that the local velocity structure may have contributed significantly to the observed shaking. P-wave velocities range from 0.9 to 1.65 km/sec, and S-wave velocities range from 0.20 and 0.6 km/sec for the upper 70 m. We also found evidence for a local S-wave low-velocity zone (LVZ) beneath the top of the hill. The LVZ underlies a CDMG strong-motion recording site at depths between 25 and 60 m below ground surface (BGS). Our velocity model is consistent with the near-surface (<30 m) P- and S-wave velocities and Poisson's ratios measured in a nearby (<30 m) borehole. High Poisson's ratios (0.477 to 0.494) and S-wave attenuation within the LVZ suggest that the LVZ may be composed of highly saturated shales of the Modelo Formation. Because the lateral dimensions of the LVZ approximately correspond to the areas of strongest shaking, we suggest that the highly saturated zone may have contributed to localized strong shaking. Rock sites are generally considered to be ideal locations for site response in urban areas; however, localized, highly saturated rock sites may be a hazard in urban areas that requires further investigation.

scholarly journals A method of geo-acoustic parameter inversion in shallow sea by the Bayesian theory and the acoustic pressure field

2019 ◽  
Vol 283 ◽  
pp. 06003
Author(s):  
Guangxue Zheng ◽  
Hanhao Zhu ◽  
Jun Zhu
Keyword(s):  
Sound Pressure ◽  
Pressure Field ◽  
Wave Attenuation ◽  
Acoustic Parameter ◽  
Bayesian Theory ◽  
P Wave ◽  
Shallow Sea ◽  
S Wave ◽  
Wave Velocities ◽  
Parameter Inversion

A method of geo-acoustic parameter inversion based on the Bayesian theory is proposed for the acquisition of acoustic parameters in shallow sea with the elastic seabed. Firstly, the theoretical prediction value of the sound pressure field is calculated by the fast field method (FFM). According to the Bayesian theory, we establish the misfit function between the measured sound pressure field and the theoretical pressure field. It is under the assumption of Gaussian data errors which are in line with the likelihood function. Finally, the posterior probability density (PPD) of parameters is given as the result of inversion. Our research is conducted in the light of Metropolis sample rules. Apart from numerical simulations, a scaled model experiment has been taken in the laboratory tank. The results of numerical simulations and tank experiments show that sound pressure field calculated by the result of inversion is consistent with the measured sound pressure field. Besides, s-wave velocities, p-wave velocities and seafloor density have fewer uncertainties and are more sensitive to complex sound pressure than s-wave attenuation and p-wave attenuation. The received signals calculated by inversion results are keeping with received signals in the experiment which verify the effectiveness of this method.

NEW DIRECTIONS IN GEOSCIENCE TECHNOLOGY FOR PETROLEUM EXPLORATION IN THE NEW AGE

1994 ◽  
Vol 34 (1) ◽  
pp. 189
Author(s):  
T. L. Burnett
Keyword(s):  
Oil And Gas ◽  
Regional Scale ◽  
Seismic Energy ◽  
Oil And Gas Industry ◽  
Transport Processes ◽  
Source Rocks ◽  
P Wave ◽  
Petroleum Exploration ◽  
S Wave ◽  
Seismic Acquisition

As economics of the oil and gas industry become more restrictive, the need for new means of improving exploration risks and reducing expenses is becoming more acute. Partnerships between industry and academia are making significant improvements in four general areas: Seismic acquisition, reservoir characterisation, quantitative structural modelling, and geochemical inversion.In marine seismic acquisition the vertical cable concept utilises hydrophones suspended at fixed locations vertically within the water column by buoys. There are numerous advantages of vertical cable technology over conventional 3-D seismic acquisition. In a related methodology, 'Borehole Seismic', seismic energy is passed between wells and valuable information on reservoir geometry, porosity, lithology, and oil saturation is extracted from the P-wave and S-wave data.In association with seismic methods of determining the external geometry and the internal properties of a reservoir, 3-dimensional sedimentation-simulation models, based on physical, hydrologic, erosional and transport processes, are being utilised for stratigraphic analysis. In addition, powerful, 1-D, coupled reaction-transport models are being used to simulate diagenesis processes in reservoir rocks.At the regional scale, the bridging of quantitative structural concepts with seismic interpretation has led to breakthroughs in structural analysis, particularly in complex terrains. Such analyses are becoming more accurate and cost effective when tied to highly advanced, remote-sensing, multi-spectral data acquisition and image processing technology. Emerging technology in petroleum geochemistry, enables geoscientists to infer the character, age, maturity, identity and location of source rocks from crude oil characteristics ('Geochemical Inversion') and to better estimate hydrocarbon-supply volumetrics. This can be invaluable in understanding petroleum systems and in reducing exploration risks and associated expenses.

EFFECT OF ALIGNMENT ON THE QUALITY OF BENDER ELEMENT PROCEDURE

2015 ◽  
Vol 76 (2) ◽  
Author(s):  
Badee Alshameri ◽  
Ismail Bakar ◽  
Aziman Madun ◽  
Edy Tonnizam Mohamad
Keyword(s):  
Vertical Axis ◽  
Poor Quality ◽  
P Wave ◽  
Secondary Wave ◽  
Horizontal Distance ◽  
Bender Element ◽  
S Wave ◽  
Testing Procedures ◽  
Wave Velocities

One of the main geophysical tools (seismic tools) in the laboratory is the bender element. This tool can be used to measure some dynamic soil properties (e.g. shear and Young’s modulus). However, even if it relatively simple to use the bender element, inconsistent testing procedures can cause poor quality in the bender element data. One of the bender element procedure that always neglected is the alignment (different positions of bender element receiver to the transmitter in the vertical axis). The alignment effect was evaluated via changing the horizontal distance between transmitter and receiver starting from 0 to 110 mm for two sizes of the sample's thickness (i.e. 63.17 mm and 91.51 mm). Five methods were applied to calculate the travel times. Those methods were as the following: visually, first-peak, maximum-peak, CCexcel and CCGDS. In general, the experiments indicated uncertain results for both of the P-wave (primary wave) and S-wave (secondary wave) velocities at zone of Dr:D above 0.5:1 (where Dr is the horizontal distance of the receiver from the vertical axis and D is the thickness of the sample). On the other hand, both the visual and first-peak methods show the wave velocities results are higher than obtained from other methods. However, the ratio between the amplitude of transmitter signals to receiver amplitude signal was taken to calculate the damping-slope of the P-wave and S-wave. Thus the results from damping slope show steeply slope when the ratio of  Dr:D is above 0.5:1 compare with gentle slope below ratio 0.5:1 at the sample with thickness equal to 91.51 mm, while there is no variation at a slope in sample with thickness equal to 63.17 mm.

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