SV-P: A potential viable alternative to mode-converted P-SV seismic data for reservoir characterization

2017 ◽  
Vol 5 (4) ◽  
pp. T579-T589 ◽  
Author(s):  
Menal Gupta ◽  
Bob Hardage
Keyword(s):  
Seismic Data ◽  
Seismic Analysis ◽  
Joint Inversion ◽  
P Wave ◽  
S Wave ◽  
Converted Wave ◽  
Important Distinction ◽  
Wave Data ◽  
Multicomponent Seismic ◽  
Injection Zone

P-SV seismic acquisition requires 3C geophones and thus has greater cost compared with conventional P-P data. However, some companies justify this added cost because P-SV data provide an independent set of S-wave measurements, which can increase the reliability of subsurface property estimation. This study investigates SV-P data generated by a vertical vibrator and recorded by vertical geophones as a cost-effective alternative to traditional P-SV data. To evaluate the efficacy of the SV-P mode relative to the P-P and P-SV modes, multicomponent seismic data from Wellington Field, Kansas, were interpreted. P-P amplitude variation with offset (AVO) gathers and stacked SV-P seismic data were jointly inverted to estimate elastic properties, which were compared with the estimates obtained from joint inversion of P-P AVO gathers, stacked P-SV seismic data, and inversion of P-P AVO gathers data. All inversions provide identical P-impedance characteristics. However, a significant improvement in S-impedance estimates is observed when P-P and converted wave data (either SV-P or P-SV) are inverted jointly, compared with P-P inversion results alone. In the Arbuckle interval, which is being considered for [Formula: see text] injection, use of converted-wave data clearly demarcates the Middle Arbuckle baffle zone and the Lower Arbuckle injection zone, with the latter having low P- and S-impedances. These observations, although consistent with other well-based geologic evidence, are absent on P-P-only inversion results. No major difference in the inversion results is seen when SV-P data are used instead of P-SV data. Moreover, we determine for the first time by comparing the SV-P image obtained from vertical-vibrator data and the SV-P image obtained from horizontal-vibrator data that both data image subsurface geology equivalently, except for the important distinction that the former contains more valuable higher frequencies than the latter. Because legacy P-wave data can be reprocessed to extract the SV-P mode, using SV-P data can provide a unique way to perform multicomponent seismic analysis.

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.

Poststack, prestack, and joint inversion of P- and S-wave data for Morrow A sandstone characterization

2015 ◽  
Vol 3 (3) ◽  
pp. SZ59-SZ92 ◽  
Author(s):  
Paritosh Singh ◽  
Thomas L. Davis ◽  
Bryan DeVault
Keyword(s):  
Wave Velocity ◽  
Seismic Data ◽  
Joint Inversion ◽  
Wave Impedance ◽  
P Wave ◽  
S Wave ◽  
Wave Data ◽  
Wave Inversion ◽  
Density Maps ◽  
P Wave Velocity

Exploration for oil-bearing Morrow sandstones using conventional seismic data/methods has a startlingly low success rate of only 3%. The S-wave velocity contrast between the Morrow shale and A sandstone is strong compared with the P-wave velocity contrast, and, therefore, multicomponent seismic data could help to characterize these reservoirs. The SV and SH data used in this study are generated using S-wave data from horizontal source and horizontal receiver recording. Prestack P- and S-wave inversions, and joint P- and S-wave inversions, provide estimates of P- and S-wave impedances, and density for characterization of the Morrow A sandstone. Due to the weak P-wave amplitude-versus-angle response at the Morrow A sandstone top, the density and S-wave impedance estimated from joint P- and S-wave inversions were inferior to the prestack S-wave inversion. The inversion results were compared with the Morrow A sandstone thickness and density maps obtained from well logs to select the final impedance and density volume for interpretation. The P-wave impedance estimated from prestack P-wave data, as well as density and S-wave impedance estimated from prestack SV‐wave data were used to identify the distribution, thickness, quality, and porosity of the Morrow A sandstone. The stratal slicing method was used to get the P- and S-wave impedances and density maps. The S-wave impedance characterizes the Morrow A sandstone distribution better than the P-wave impedance throughout the study area. Density estimation from prestack inversion of SV data was able to distinguish between low- and high-quality reservoirs. The porosity volume was estimated from the density obtained from prestack SV-wave inversion. We found some possible well locations based on the interpretation.

Converted-wave model building and imaging based on common-focus-point methodology

Geophysics ◽  
2020 ◽  
Vol 85 (6) ◽  
pp. U139-U149
Author(s):  
Hongwei Liu ◽  
Mustafa Naser Al-Ali ◽  
Yi Luo
Keyword(s):  
Elastic Wave ◽  
Wave Velocity ◽  
Seismic Data ◽  
Model Building ◽  
P Wave ◽  
Rock Properties ◽  
S Wave ◽  
Converted Wave ◽  
Seismic Images ◽  
Focus Point

Seismic images can be viewed as photographs for underground rocks. These images can be generated from different reflections of elastic waves with different rock properties. Although the dominant seismic data processing is still based on the acoustic wave assumption, elastic wave processing and imaging have become increasingly popular in recent years. A major challenge in elastic wave processing is shear-wave (S-wave) velocity model building. For this reason, we have developed a sequence of procedures for estimating seismic S-wave velocities and the subsequent generation of seismic images using converted waves. We have two main essential new supporting techniques. The first technique is the decoupling of the S-wave information by generating common-focus-point gathers via application of the compressional-wave (P-wave) velocity on the converted seismic data. The second technique is to assume one common VP/ VS ratio to approximate two types of ratios, namely, the ratio of the average earth layer velocity and the ratio of the stacking velocity. The benefit is that we reduce two unknown ratios into one, so it can be easily scanned and picked in practice. The PS-wave images produced by this technology could be aligned with the PP-wave images such that both can be produced in the same coordinate system. The registration between the PP and PS images provides cross-validation of the migrated structures and a better estimation of underground rock and fluid properties. The S-wave velocity, computed from the picked optimal ratio, can be used not only for generating the PS-wave images, but also to ensure well registration between the converted-wave and P-wave images.

Simultaneous inversion of PP and PS seismic data

Geophysics ◽  
2006 ◽  
Vol 71 (3) ◽  
pp. R1-R10 ◽  
Author(s):  
Helene Hafslund Veire ◽  
Martin Landrø
Keyword(s):  
Seismic Data ◽  
Reservoir Characterization ◽  
Model Building ◽  
Joint Inversion ◽  
P Wave ◽  
Inversion Method ◽  
System Of Equations ◽  
S Wave ◽  
Data Set ◽  
Simultaneous Inversion

Elastic parameters derived from seismic data are valuable input for reservoir characterization because they can be related to lithology and fluid content of the reservoir through empirical relationships. The relationship between physical properties of rocks and fluids and P-wave seismic data is nonunique. This leads to large uncertainties in reservoir models derived from P-wave seismic data. Because S- waves do not propagate through fluids, the combined use of P-and S-wave seismic data might increase our ability to derive fluid and lithology effects from seismic data, reducing the uncertainty in reservoir characterization and thereby improving 3D reservoir model-building. We present a joint inversion method for PP and PS seismic data by solving approximated linear expressions of PP and PS reflection coefficients simultaneously using a least-squares estimation algorithm. The resulting system of equations is solved by singular-value decomposition (SVD). By combining the two independent measurements (PP and PS seismic data), we stabilize the system of equations for PP and PS seismic data separately, leading to more robust parameter estimation. The method does not require any knowledge of PP and PS wavelets. We tested the stability of this joint inversion method on a 1D synthetic data set. We also applied the methodology to North Sea multicomponent field data to identify sand layers in a shallow formation. The identified sand layers from our inverted sections are consistent with observations from nearby well logs.

Predicting reservoir quality in the Bakken Formation, North Dakota, using petrophysics and 3C seismic data

2020 ◽  
Vol 8 (4) ◽  
pp. T851-T868
Author(s):  
Andrea G. Paris ◽  
Robert R. Stewart
Keyword(s):  
North Dakota ◽  
Seismic Data ◽  
Rock Physics ◽  
P Wave ◽  
Well Logs ◽  
Reservoir Quality ◽  
Bakken Formation ◽  
S Wave ◽  
Converted Wave ◽  
Wave Velocities

Combining rock-property analysis with multicomponent seismic imaging can be an effective approach for reservoir quality prediction in the Bakken Formation, North Dakota. The hydrocarbon potential of shale is indicated on well logs by low density, high gamma-ray response, low compressional-wave (P-wave) and shear-wave (S-wave) velocities, and high neutron porosity. We have recognized the shale intervals by cross plotting sonic velocities versus density. Intervals with total organic carbon (TOC) content higher than 10 wt% deviate from lower TOC regions in the density domain and exhibit slightly lower velocities and densities (<2.30 g/cm3). We consider TOC to be the principal factor affecting changes in the density and P- and S-wave velocities in the Bakken shales, where VP/ VS ranges between 1.65 and 1.75. We generate the synthetic seismic data using an anisotropic version of the Zoeppritz equations, including estimated Thomsen’s parameters. For the tops of the Upper and Lower Bakken, the amplitude shows a negative intercept and a positive gradient, which corresponds to an amplitude variation with offset of class IV. The P-impedance error decreases by 14% when incorporating the converted-wave information in the inversion process. A statistical approach using multiattribute analysis and neural networks delimits the zones of interest in terms of P-impedance, density, TOC content, and brittleness. The inverted and predicted results show reasonable correlations with the original well logs. The integration of well log analysis, rock physics, seismic modeling, constrained inversions, and statistical predictions contributes to identifying the areas of highest reservoir quality within the Bakken Formation.

scholarly journals Advantages of Shear Wave Seismic in Morrow Sandstone Detection

2011 ◽  
Vol 2011 ◽  
pp. 1-16 ◽  
Author(s):  
Paritosh Singh ◽  
Thomas Davis
Keyword(s):  
Shear Wave ◽  
Pure Shear ◽  
High Amplitude ◽  
Compressional Wave ◽  
Side Lobe ◽  
P Wave ◽  
S Wave ◽  
Converted Wave ◽  
P Waves ◽  
Wave Data

The Upper Morrow sandstones in the western Anadarko Basin have been prolific oil producers for more than five decades. Detection of Morrow sandstones is a major problem in the exploration of new fields and the characterization of existing fields because they are often very thin and laterally discontinuous. Until recently compressional wave data have been the primary resource for mapping the lateral extent of Morrow sandstones. The success with compressional wave datasets is limited because the acoustic impedance contrast between the reservoir sandstones and the encasing shales is small. Here, we have performed full waveform modeling study to understand the Morrow sandstone signatures on compressional wave (P-wave), converted-wave (PS-wave) and pure shear wave (S-wave) gathers. The contrast in rigidity between the Morrow sandstone and surrounding shale causes a strong seismic expression on the S-wave data. Morrow sandstone shows a distinct high amplitude event in pure S-wave modeled gathers as compared to the weaker P- and PS-wave events. Modeling also helps in understanding the adverse effect of interbed multiples (due to shallow high velocity anhydrite layers) and side lobe interference effects at the Morrow level. Modeling tied with the field data demonstrates that S-waves are more robust than P-waves in detecting the Morrow sandstone reservoirs.

Elastic full waveform inversion of multicomponent ocean-bottom cable seismic data: Application to Alba Field, U. K. North Sea

Geophysics ◽  
2010 ◽  
Vol 75 (6) ◽  
pp. R109-R119 ◽  
Author(s):  
Timothy J. Sears ◽  
Penny J. Barton ◽  
Satish C. Singh
Keyword(s):  
Wave Velocity ◽  
Seismic Data ◽  
Waveform Inversion ◽  
P Wave ◽  
Full Waveform Inversion ◽  
Ocean Bottom ◽  
S Wave ◽  
Wave Data ◽  
Data Inversion ◽  
Full Waveform

Elastic full waveform inversion of multichannel seismic data represents a data-driven form of analysis leading to direct quantification of the subsurface elastic parameters in the depth domain. Previous studies have focused on marine streamer data using acoustic or elastic inversion schemes for the inversion of P-wave data. In this paper, P- and S-wave velocities are inverted for using wide-angle multicomponent ocean-bottom cable (OBC) seismic data. Inversion is undertaken using a two-dimensional elastic algorithm operating in the time domain, which allows accurate modeling and inversion of the full elastic wavefield, including P- and mode-converted PS-waves and their respective amplitude variation with offset (AVO) responses. Results are presented from the application of this technique to an OBC seismic data set from the Alba Field, North Sea. After building an initial velocity model and extracting a seismic wavelet, the data are inverted instages. In the first stage, the intermediate wavelength P-wave velocity structure is recovered from the wide-angle data and then the short-scale detail from near-offset data using P-wave data on the [Formula: see text] (vertical geophone) component. In the second stage, intermediate wavelengths of S-wave velocity are inverted for, which exploits the information captured in the P-wave’s elastic AVO response. In the third stage, the earlier models are built on to invert mode-converted PS-wave events on the [Formula: see text] (horizontal geophone) component for S-wave velocity, targeting first shallow and then deeper structure. Inversion of [Formula: see text] alone has been able to delineate the Alba Field in P- and S-wave velocity, with the main field and outlier sands visible on the 2D results. Inversion of PS-wave data has demonstrated the potential of using converted waves to resolve shorter wavelength detail. Even at the low frequencies [Formula: see text] inverted here, improved spatial resolution was obtained by inverting S-wave data compared with P-wave data inversion results.

Anisotropy effects in P-wave and SH-wave stacking velocities contain information on lithology

Geophysics ◽  
1986 ◽  
Vol 51 (3) ◽  
pp. 661-672 ◽  
Author(s):  
D. F. Winterstein
Keyword(s):  
Seismic Data ◽  
P Wave ◽  
Sh Wave ◽  
S Wave ◽  
Velocity Anisotropy ◽  
Midland Basin ◽  
Wave Data ◽  
Event Times ◽  
Interval Velocities ◽  
Constituent Layer

Depths calculated from S-wave stacking velocities and event times almost always exceed actual depths, sometimes by as much as 25 percent. In contrast, depths from corresponding P-wave information are often within 10 percent of actual depths. Discrepancies in depths calculated from P- and S-wave data are attributed to velocity anisotropy, a property of sedimentary rocks that noticeably affects S-wave moveout curves but leaves the P-wave relatively unaffected. Two careful studies show that discrepancies in depths, and hence in constituent layer thicknesses, correlate with lithology. Discrepancies ranged from an average of 13 percent (Midland basin) to greater than 40 percent (Paloma field) in shales, but were within expected errors in massive sandstones or carbonates. Hence anisotropy effects are indicators of lithology. Analysis of seismic data involved determining interval velocities from stacking velocities, calculating layer thicknesses, and then comparing layer thicknesses from S-wave data with thicknesses from P-wave data. When the S-wave thicknesses were significantly greater than the P-wave (i.e., outside the range of expected errors), I concluded the layer was anisotropic. I illustrate the technique with data from the Paloma field project of the Conoco Shear Wave Group Shoot.

Some comments on common-asymptotic-conversion-point (CACP) sorting of converted-wave data in isotropic, laterally inhomogeneous media

Geophysics ◽  
2005 ◽  
Vol 70 (3) ◽  
pp. U29-U36 ◽  
Author(s):  
Mirko van der Baan
Keyword(s):  
Wave Velocity ◽  
Velocity Ratio ◽  
P Wave ◽  
Pure Mode ◽  
S Wave ◽  
Converted Wave ◽  
Wave Data ◽  
Conversion Point ◽  
Isotropic Media ◽  
Zero Offset

Common-midpoint (CMP) sorting of pure-mode data in arbitrarily complex isotropic or anisotropic media leads to moveout curves that are symmetric around zero offset. This greatly simplifies velocity determination of pure-mode data. Common-asymptotic-conversion-point (CACP) sorting of converted-wave data, on the other hand, only centers the apexes of all traveltimes around zero offset in arbitrarily complex but isotropic media with a constant P-wave/S-wave velocity ratio everywhere. A depth-varying CACP sorting may therefore be required to position all traveltimes properly around zero offset in structurally complex areas. Moreover, converted-wave moveout is nearly always asymmetric and nonhyperbolic. Thus, positive and negative offsets need to be processed independently in a 2D line, and 3D data volumes are to be divided in common azimuth gathers. All of these factors tend to complicate converted-wave velocity analysis significantly.

scholarly journals Study of Converted-Wave Modeling: AVO Application for Shallow Gas Models

2018 ◽  
Vol 16 (2) ◽  
pp. 19 ◽  
Author(s):  
Natashia Christy Viony ◽  
Wahyu Triyoso
Keyword(s):  
Data Processing ◽  
Vertical Component ◽  
Hydrocarbon Exploration ◽  
P Wave ◽  
Diffraction Effect ◽  
S Wave ◽  
Converted Wave ◽  
Gas Reservoir ◽  
Shallow Gas ◽  
Wave Data

The application of converted-wave seismic method in hydrocarbon exploration has increased significantly. Since the conventional seismic ceases to provide an adequate result in complex geology area and it provides an ambiguous brightspot response. The main principle is that an incident P-wave produces reflected and converted P and SV wave when the downgoing P-wave impinges on an interface. Converted-wave seismic uses the multicomponent receiver that records both of vertical component and horizontal component. The vertical component is assumed to correspond to the compressional PP wave and the horizontal correspond to the PS converted-wave. In this research, a synthetic model with the shallow gas and the salt dome below are constructed. The purpose of this study is to analyze the brightspot due to the presence of shallow gas and its effect to the quality of PP and PS wave reflection below the gas zone. To achieve the goal, both vertical and horizontal seismic data processing are performed. In horizontal data processing, the best gamma function (Vp/Vs) value is estimated to produce the better and reliable image. The result shows that the brightspot response in conventional data doesn’t exist in converted-wave data and the imaging below the gas zone in converted-wave data is better than the conventional due to the attenuation and diffraction effect that caused by gas column. Processing is followed by AVO analysis to compare the AVO response of PP and PS data in characterizing gas reservoir. Both PP and PS AVO curve shows the consistency with synthetic AVO from well data. Gas reservoir is a class 1 AVO anomaly with positive intercept and negative gradient on PP data. However, PS AVO curve does not refer any anomaly. It is because S-wave is not sensitive to the existence of rock saturant.

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