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.

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.

Using Seismic Data to Predict Shale Pore Pressure and Overpressure Sweet Spots: A Case Study from the Lower Silurian Longmaxi Formation in Sichuan Basin, China

2021 ◽  
Author(s):  
Sheng Chen ◽  
Qingcai Zeng ◽  
Xiujiao Wang ◽  
Qing Yang ◽  
Chunmeng Dai ◽  
...  
Keyword(s):  
Prediction Model ◽  
Wave Velocity ◽  
Shale Gas ◽  
Seismic Data ◽  
P Wave ◽  
Formation Pressure ◽  
S Wave ◽  
P Wave Velocity ◽  
New Formation ◽  
Sweet Spots

Abstract Practices of marine shale gas exploration and development in south China have proved that formation overpressure is the main controlling factor of shale gas enrichment and an indicator of good preservation condition. Accurate prediction of formation pressure before drilling is necessary for drilling safety and important for sweet spots predicting and horizontal wells deploying. However, the existing prediction methods of formation pore pressures all have defects, the prediction accuracy unsatisfactory for shale gas development. By means of rock mechanics analysis and related formulas, we derived a formula for calculating formation pore pressures. Through regional rock physical analysis, we determined and optimized the relevant parameters in the formula, and established a new formation pressure prediction model considering P-wave velocity, S-wave velocity and density. Based on regional exploration wells and 3D seismic data, we carried out pre-stack seismic inversion to obtain high-precision P-wave velocity, S-wave velocity and density data volumes. We utilized the new formation pressure prediction model to predict the pressure and the spatial distribution of overpressure sweet spots. Then, we applied the measured pressure data of three new wells to verify the predicted formation pressure by seismic data. The result shows that the new method has a higher accuracy. This method is qualified for safe drilling and prediction of overpressure sweet spots for shale gas development, so it is worthy of promotion.

Nonlinear two‐dimensional elastic inversion of multioffset seismic data

Geophysics ◽  
1987 ◽  
Vol 52 (9) ◽  
pp. 1211-1228 ◽  
Author(s):  
Peter Mora
Keyword(s):  
Wave Equation ◽  
Wave Velocity ◽  
A Priori ◽  
Gaussian Model ◽  
Wave Impedance ◽  
P Wave ◽  
S Wave ◽  
Elastic Wave Equation ◽  
Gradient Direction ◽  
P Wave Velocity

The treatment of multioffset seismic data as an acoustic wave field is becoming increasingly disturbing to many geophysicists who see a multitude of wave phenomena, such as amplitude‐offset variations and shearwave events, which can only be explained by using the more correct elastic wave equation. Not only are such phenomena ignored by acoustic theory, but they are also treated as undesirable noise when they should be used to provide extra information, such as S‐wave velocity, about the subsurface. The problems of using the conventional acoustic wave equation approach can be eliminated via an elastic approach. In this paper, equations have been derived to perform an inversion for P‐wave velocity, S‐wave velocity, and density as well as the P‐wave impedance, S‐wave impedance, and density. These are better resolved than the Lamé parameters. The inversion is based on nonlinear least squares and proceeds by iteratively updating the earth parameters until a good fit is achieved between the observed data and the modeled data corresponding to these earth parameters. The iterations are based on the preconditioned conjugate gradient algorithm. The fundamental requirement of such a least‐squares algorithm is the gradient direction which tells how to update the model parameters. The gradient direction can be derived directly from the wave equation and it may be computed by several wave propagations. Although in principle any scheme could be chosen to perform the wave propagations, the elastic finite‐ difference method is used because it directly simulates the elastic wave equation and can handle complex, and thus realistic, distributions of elastic parameters. This method of inversion is costly since it is similar to an iterative prestack shot‐profile migration. However, it has greater power than any migration since it solves for the P‐wave velocity, S‐wave velocity, and density and can handle very general situations including transmission problems. Three main weaknesses of this technique are that it requires fairly accurate a priori knowledge of the low‐ wavenumber velocity model, it assumes Gaussian model statistics, and it is very computer‐intensive. All these problems seem surmountable. The low‐wavenumber information can be obtained either by a prior tomographic step, by the conventional normal‐moveout method, by a priori knowledge and empirical relationships, or by adding an additional inversion step for low wavenumbers to each iteration. The Gaussian statistics can be altered by preconditioning the gradient direction, perhaps to make the solution blocky in appearance like well logs, or by using large model variances in the inversion to reduce the effect of the Gaussian model constraints. Moreover, with some improvements to the algorithm and more parallel computers, it is hoped the technique will soon become routinely feasible.

Elastic reflectivity vectors and the geometry of intercept-gradient crossplots

2019 ◽  
Vol 38 (10) ◽  
pp. 762-769
Author(s):  
Patrick Connolly
Keyword(s):  
Elastic Property ◽  
Elastic Properties ◽  
Wave Velocity ◽  
Seismic Data ◽  
Measurement Errors ◽  
Rock Physics ◽  
P Wave ◽  
S Wave ◽  
P Wave Velocity ◽  
Well Log Analysis

Reflectivities of elastic properties can be expressed as a sum of the reflectivities of P-wave velocity, S-wave velocity, and density, as can the amplitude-variation-with-offset (AVO) parameters, intercept, gradient, and curvature. This common format allows elastic property reflectivities to be expressed as a sum of AVO parameters. Most AVO studies are conducted using a two-term approximation, so it is helpful to reduce the three-term expressions for elastic reflectivities to two by assuming a relationship between P-wave velocity and density. Reduced to two AVO components, elastic property reflectivities can be represented as vectors on intercept-gradient crossplots. Normalizing the lengths of the vectors allows them to serve as basis vectors such that the position of any point in intercept-gradient space can be inferred directly from changes in elastic properties. This provides a direct link between properties commonly used in rock physics and attributes that can be measured from seismic data. The theory is best exploited by constructing new seismic data sets from combinations of intercept and gradient data at various projection angles. Elastic property reflectivity theory can be transferred to the impedance domain to aid in the analysis of well data to help inform the choice of projection angles. Because of the effects of gradient measurement errors, seismic projection angles are unlikely to be the same as theoretical angles or angles derived from well-log analysis, so seismic data will need to be scanned through a range of angles to find the optimum.

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.

A strategy for nonlinear elastic inversion of seismic reflection data

Geophysics ◽  
1986 ◽  
Vol 51 (10) ◽  
pp. 1893-1903 ◽  
Author(s):  
Albert Tarantola
Keyword(s):  
Wave Velocity ◽  
Seismic Reflection ◽  
Wave Impedance ◽  
P Wave ◽  
S Wave ◽  
Seismic Reflection Data ◽  
S Waves ◽  
Wave Velocities ◽  
Reflection Data ◽  
P Wave Velocity

The problem of interpretation of seismic reflection data can be posed with sufficient generality using the concepts of inverse theory. In its roughest formulation, the inverse problem consists of obtaining the Earth model for which the predicted data best fit the observed data. If an adequate forward model is used, this best model will give the best images of the Earth’s interior. Three parameters are needed for describing a perfectly elastic, isotropic, Earth: the density ρ(x) and the Lamé parameters λ(x) and μ(x), or the density ρ(x) and the P-wave and S-wave velocities α(x) and β(x). The choice of parameters is not neutral, in the sense that although theoretically equivalent, if they are not adequately chosen the numerical algorithms in the inversion can be inefficient. In the long (spatial) wavelengths of the model, adequate parameters are the P-wave and S-wave velocities, while in the short (spatial) wavelengths, P-wave impedance, S-wave impedance, and density are adequate. The problem of inversion of waveforms is highly nonlinear for the long wavelengths of the velocities, while it is reasonably linear for the short wavelengths of the impedances and density. Furthermore, this parameterization defines a highly hierarchical problem: the long wavelengths of the P-wave velocity and short wavelengths of the P-wave impedance are much more important parameters than their counterparts for S-waves (in terms of interpreting observed amplitudes), and the latter are much more important than the density. This suggests solving the general inverse problem (which must involve all the parameters) by first optimizing for the P-wave velocity and impedance, then optimizing for the S-wave velocity and impedance, and finally optimizing for density. The first part of the problem of obtaining the long wavelengths of the P-wave velocity and the short wavelengths of the P-wave impedance is similar to the problem solved by present industrial practice (for accurate data interpretation through velocity analysis and “prestack migration”). In fact, the method proposed here produces (as a byproduct) a generalization to the elastic case of the equations of “prestack acoustic migration.” Once an adequate model of the long wavelengths of the P-wave velocity and of the short wavelengths of the P-wave impedance has been obtained, the data residuals should essentially contain information on S-waves (essentially P-S and S-P converted waves). Once the corresponding model of S-wave velocity (long wavelengths) and S-wave impedance (short wavelengths) has been obtained, and if the remaining residuals still contain information, an optimization for density should be performed (the short wavelengths of impedances do not give independent information on density and velocity independently). Because the problem is nonlinear, the whole process should be iterated to convergence; however, the information from each parameter should be independent enough for an interesting first solution.

TitleApplication of M4C P-wave and S-wave Joint Inversion in Shallow Reservoir Identification

2021 ◽  
Author(s):  
Sian Zhu ◽  
Hongtao Chen ◽  
Yongjun Hu ◽  
Feng Yang ◽  
Yubin Feng
Keyword(s):  
Wave Velocity ◽  
Oil And Gas ◽  
Joint Inversion ◽  
Wave Impedance ◽  
P Wave ◽  
Seismic Survey ◽  
Bohai Bay ◽  
S Wave ◽  
Bohai Bay Basin ◽  
Gas Reservoirs

Abstract The genetic mechanism of shallow gas reservoirs is complex, which is usually characterized by shallow burial depth, multiple types, low reserves and wide distribution, so that the inversion based on P-wave data alone may be ambiguous. For shallow reservoir with large lateral variation, it is hard to accurately predict oil and water distribution by conventional P-wave prestack inversion. Marine four-component (M4C) P-wave and S-wave joint inversion can solve the problem effectively. M4C seismic survey collects P-wave and S-wave seismic data. An initial model can be established based on fine structural interpretation of P-wave and S-wave data and S-wave compression pattern matching. It lays a good foundation for subsequent P-wave and S-wave joint inversion. Based on the P-wave seismic record equation proposed by Fatti et al., a seismic record equation from poststack P-wave and S-wave joint inversion was derived according to the relationship among reflection coefficient, P-wave impedance, S-wave impedance and density, then important lithologic parameters (P-wave impedance, S-wave impedance and density) were calculated, and finally the ratio of P-wave velocity to S-wave velocity which is more sensitive to oil and gas was obtained. According to the ratio of P-wave velocity to S-wave velocity, the oil and gas distribution was predicted in shallow Bohai Bay Basin. Application has proven that the inversion can well reflect the fluid distribution, and the coincidence between the inversion results and the drilling data is up to 85%.M4C seismic survey was conducted for the first time to the shallow oil and gas reservoirs with rapid lateral variation in the Bohai Bay Basin, and collected raw P-wave and S-wave seismic data. Based on the data, the precision and reliability of P-wave and S-wave joint inversion was improved. The results provided strong technical support to the reserves and production increase in the Bohai Bay Basin.

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.

Interpretation of AVO anomalies

Geophysics ◽  
2010 ◽  
Vol 75 (5) ◽  
pp. 75A3-75A13 ◽  
Author(s):  
Douglas J. Foster ◽  
Robert G. Keys ◽  
F. David Lane
Keyword(s):  
Wave Velocity ◽  
Seismic Data ◽  
P Wave ◽  
Rock Properties ◽  
Light Hydrocarbons ◽  
S Wave ◽  
Amplitude Variation With Offset ◽  
P Wave Velocity ◽  
Fluid Properties ◽  
Porosity Changes

We investigate the effects of changes in rock and fluid properties on amplitude-variation-with-offset (AVO) responses. In the slope-intercept domain, reflections from wet sands and shales fall on or near a trend that we call the fluid line. Reflections from the top of sands containing gas or light hydrocarbons fall on a trend approximately parallel to the fluid line; reflections from the base of gas sands fall on a parallel trend on the opposing side of the fluid line. The polarity standard of the seismic data dictates whether these reflections from the top of hydrocarbon-bearing sands are below or above the fluid line. Typically, rock properties of sands and shales differ, and therefore reflections from sand/shale interfaces are also displaced from the fluid line. The distance of these trends from the fluid line depends upon the contrast of the ratio of P-wave velocity [Formula: see text] and S-wave velocity [Formula: see text]. This ratio is a function of pore-fluid compressibility and implies that distance from the fluid line increases with increasing compressibility. Reflections from wet sands are closer to the fluid line than hydrocarbon-related reflections. Porosity changes affect acoustic impedance but do not significantly impact the [Formula: see text] contrast. As a result, porosity changes move the AVO response along trends approximately parallel to the fluid line. These observations are useful for interpreting AVO anomalies in terms of fluids, lithology, and porosity.

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