Interpreter's Corner

2021 ◽  
Vol 40 (6) ◽  
pp. 454-459
Author(s):  
David J. Went
Keyword(s):  
Seismic Data ◽  
Incidence Angle ◽  
Seismic Inversion ◽  
P Wave ◽  
Data Sets ◽  
Oil Sand ◽  
S Wave ◽  
Seismic Line ◽  
The Difference ◽  
Amplitude Variation With Angle

Global empirical relationships of P-wave to S-wave and density for sandstones and shales are used to model two-term amplitude variation with angle at various depths of burial in a typically compacting siliciclastic basin. Data from the normally pressured Tertiary strata of Judd Basin, Atlantic Margin, West of Shetland, are used as a control. For a typical prospect depth of 1750 m below mudline, forward models of angle-dependent reflectivity reveal that discrimination of lithology (shale and brine sand) and fluid (oil sand) is optimally resolved at a 47° incidence angle (θ). This is equivalent to an angle of 28° on an intercept-gradient crossplot. Repeat experiments at other depths produce similar results but with the angle for optimal lithology and fluid determination shifting slightly with increasing depth. Background trends in seismic data crossplots of intercept versus gradient are typically overprinted by noise that has a disproportionate effect on the gradient. This study suggests that the difference between the noise and background rock-property trend is relatively small, such that in most modern seismic data sets, anomalies should be identifiable on time-windowed crossplots and equivalent weighted stacks. It is proposed that a seismic inversion for relative extended elastic impedance at a 45° incidence angle should capture most anomalies of interest in frontier basins with simple burial histories. An example is illustrated from a seismic line in Mozambique.

Three-parameter prestack seismic inversion based on L1-2 minimization

Geophysics ◽  
2019 ◽  
Vol 84 (5) ◽  
pp. R753-R766 ◽  
Author(s):  
Lingqian Wang ◽  
Hui Zhou ◽  
Yufeng Wang ◽  
Bo Yu ◽  
Yuanpeng Zhang ◽  
...  
Keyword(s):  
Wave Velocity ◽  
Seismic Inversion ◽  
Thin Layers ◽  
P Wave ◽  
S Wave ◽  
Reservoir Prediction ◽  
Linear Inversion ◽  
Prestack Inversion ◽  
The Difference ◽  
Predictive Filtering

Prestack inversion has become a common approach in reservoir prediction. At present, the critical issue in the application of seismic inversion is the estimation of elastic parameters in the thin layers and weak reflectors. To improve the resolution and the accuracy of the inversion results, we introduced the difference of [Formula: see text] and [Formula: see text] norms as a nearly unbiased approximation of the sparsity of a vector, denoted as the [Formula: see text] norm, to the prestack inversion. The nonconvex penalty function of the [Formula: see text] norm can be decomposed into two convex subproblems via the difference of convex algorithm, and each subproblem can be solved efficiently by the alternating direction method of multipliers. Compared with the [Formula: see text] norm regularization, the [Formula: see text] minimization can reconstruct reflectivities more accurately. In addition, the [Formula: see text]-[Formula: see text] predictive filtering was introduced to guarantee the lateral continuity of the location and the amplitude of the reflectivity series. The generalized linear inversion and [Formula: see text]-[Formula: see text] predictive filtering are combined for stable elastic impedance inversion results, and three parameters of P-wave velocity, S-wave velocity, and density can be inverted with the Bayesian linearized amplitude variation with offset inversion. The inversion results of synthetic and real seismic data demonstrate that the proposed method can effectively improve the resolution and accuracy of the inversion results.

Accuracy Comparison of Elastic Impedance Formula

2012 ◽  
Vol 466-467 ◽  
pp. 400-404
Author(s):  
Jin Zhang ◽  
Huai Shan Liu ◽  
Si You Tong ◽  
Lin Fei Wang ◽  
Bing Xu
Keyword(s):  
Seismic Data ◽  
Poisson Ratio ◽  
Seismic Inversion ◽  
P Wave ◽  
S Wave ◽  
Elastic Parameters ◽  
Accuracy Comparison ◽  
Elastic Impedance ◽  
Prestack Seismic Inversion ◽  
Lamé Coefficients

Elastic impedance (EI) inversion is one of the prestack seismic inversion methods, which can obtain P-wave and S-wave velocity, density, Poisson ratio, Lame coefficients and other elastic parameters. But there have been many EI formulas nowadays, so which formula should be used in inversion is an urgent problem. This paper divides these formulas into two categories, and use several forward modeling to test the accuracy of these EI formulas. It shows that using the first kind of EI formulas in near offset seismic data can get high precision results.

An Adaptive Stratified Joint PP and PS AVA Inversion Using Accurate Jacobian Matrix

Geophysics ◽  
2021 ◽  
pp. 1-145
Author(s):  
Xiaobo Liu ◽  
Jingyi Chen ◽  
Jing Zeng ◽  
Fuping Liu ◽  
Handong Huang ◽  
...  
Keyword(s):  
Seismic Data ◽  
Jacobian Matrix ◽  
Incidence Angle ◽  
P Wave ◽  
Rock Properties ◽  
Inversion Method ◽  
S Wave ◽  
Zoeppritz Equations ◽  
Wave Velocities ◽  
Strong Contrast

Amplitude variation with incidence angle (AVA) analysis is an essential tool for discriminating lithology in the hydrocarbon reservoirs. Compared with the traditional AVA inversion using only P-wave information, joint AVA inversion using PP and PS seismic data provides better estimation of rock properties (e.g., density, P- and S-wave velocities). At present, the most used AVA inversions depend on the approximations of Zoeppritz equations (e.g., Shuey and Aki-Richards approximations), which are not suitable for formations with strong contrast interfaces and seismic data with large incidence angles. Based on the previous derivation of accurate Jacobian matrix, we find that the sign of each partial derivative of reflection coefficient with respect to P-, S-wave velocities and density changes across the interface, represents good indicator for the reflection interfaces. Accordingly, we propose an adaptive stratified joint PP and PS AVA inversion using the accurate Jacobian matrix that can automatically obtain the layer information and can be further used as a constraint in the inversion of in-layer rock properties (density, P- and S-wave velocities). Due to the use of the exact Zoeppritz equations and accurate Jacobian matrix, this proposed inversion method is more accurate than traditional AVA inversion methods, has higher computational efficiency and can be applied to seismic wide-angle reflection data or seismic data acquired for formations with strong contrast interfaces. The model study shows that this proposed inversion method works better than the classical Shuey and Aki-Richards approximations at estimating reflection interfaces and in-layer rock properties. It also works well in handling a part of the complex Marmousi 2 model and real seismic data.

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.

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.

Near-surface scattering phenomena and implications for tunnel detection

2015 ◽  
Vol 3 (1) ◽  
pp. SF43-SF54 ◽  
Author(s):  
Shelby L. Peterie ◽  
Richard D. Miller
Keyword(s):  
Synthetic Data ◽  
P Wave ◽  
S Wave ◽  
Seismic Line ◽  
Data Set ◽  
Near Surface ◽  
Tunnel Detection ◽  
Impedance Contrast ◽  
Diffraction Imaging ◽  
Imaging Algorithms

Tunnel locations are accurately interpreted from diffraction sections of focused mode converted P- to S-wave diffractions from a perpendicular tunnel and P-wave diffractions from a nonperpendicular (oblique) tunnel. Near-surface tunnels are ideal candidates for diffraction imaging due to their small size relative to the seismic wavelength and large acoustic impedance contrast at the tunnel interface. Diffraction imaging algorithms generally assume that the velocities of the primary wave and the diffracted wave are approximately equal, and that the diffraction apex is recorded directly above the scatterpoint. Scattering phenomena from shallow tunnels with kinematic properties that violate these assumptions were observed in one field data set and one synthetic data set. We developed the traveltime equations for mode-converted and oblique diffractions and demonstrated a diffraction imaging algorithm designed for the roll-along style of acquisition. Potential processing and interpretation pitfalls specific to these diffraction types were identified. Based on our observations, recommendations were made to recognize and image mode-converted and oblique diffractions and accurately interpret tunnel depth, horizontal location, and azimuth with respect to the seismic line.

On plane‐wave decomposition: Alias removal

Geophysics ◽  
1989 ◽  
Vol 54 (10) ◽  
pp. 1339-1343 ◽  
Author(s):  
S. C. Singh ◽  
G. F. West ◽  
C. H. Chapman
Keyword(s):  
Plane Wave ◽  
Seismic Data ◽  
P Wave ◽  
S Wave ◽  
Plane Wave Decomposition ◽  
Time Migration ◽  
Time Distance ◽  
P Domain ◽  
Wave Decomposition ◽  
Rapid Modeling

The delay‐time (τ‐p) parameterization, which is also known as the plane‐wave decomposition (PWD) of seismic data, has several advantages over the more traditional time‐distance (t‐x) representation (Schultz and Claerbout, 1978). Plane‐wave seismograms in the (τ, p) domain can be used for obtaining subsurface elastic properties (P‐wave and S‐wave velocities and density as functions of depth) from inversion of the observed oblique‐incidence seismic data (e.g., Yagle and Levy, 1985; Carazzone, 1986; Carrion, 1986; Singh et al., 1989). Treitel et al. (1982) performed time migration of plane‐wave seismograms. Diebold and Stoffa (1981) used plane‐wave seismograms to derive a velocity‐depth function. Decomposing seismic data also allows more rapid modeling, since it is faster to compute synthetic seismograms in the (τ, p) than in the (t, x) domain. Unfortunately, the transformation of seismic data from the (t, x) to the (τ, p) domain may produce artifacts, such as those caused by discrete sampling, of the data in space.

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.

scholarly journals Anisotropy extraction using a deviated well in the Mahanadi Basin, East Coast of India

2019 ◽  
Vol 10 (3) ◽  
pp. 911-918
Author(s):  
Biplab Kumar Mukherjee ◽  
G. Karthikeyan ◽  
Karanpal Rawat ◽  
Hari Srivastava
Keyword(s):  
Seismic Data ◽  
East Coast ◽  
Velocity Model ◽  
P Wave ◽  
S Wave ◽  
Gardner Equation ◽  
East Coast Of India ◽  
Nonlinear Fitting ◽  
Sonic Log ◽  
Mahanadi Basin

Abstract Shale is the primary rock type in the shallow marine section of the Mahanadi Basin, East Coast of India. Shale, being intrinsically anisotropic, always affects the seismic data. Anisotropy derived from seismic and VSP has lower resolution and mostly based on P wave. The workflow discussed here uses Gardner equation to derive vertical velocity and uses a nonlinear fitting to extract the Thomsen’s parameters using both the P wave and S wave data. These parameters are used to correct the sonic log of a deviated well as well as anisotropic AVO response of the reservoir. The presence of negative delta was observed, which is believed to be affected by the presence of chloride and illite in the rock matrix. This correction can be used to update the velocity model for time–depth conversion and pore pressure modelling.

Prestack 2D parsimonious Kirchhoff depth migration of elastic seismic data

Geophysics ◽  
2011 ◽  
Vol 76 (4) ◽  
pp. S157-S164 ◽  
Author(s):  
Robert Sun ◽  
George A. McMechan
Keyword(s):  
Seismic Data ◽  
Velocity Model ◽  
P Wave ◽  
P Value ◽  
Common Source ◽  
S Wave ◽  
P Values ◽  
Depth Migration ◽  
Reflector Surface ◽  
Kirchhoff Depth Migration

We have extended prestack parsimonious Kirchhoff depth migration for 2D, two-component, reflected elastic seismic data for a P-wave source recorded at the earth’s surface. First, we separated the P-to-P reflected (PP-) waves and P-to-S converted (PS-) waves in an elastic common-source gather into P-wave and S-wave seismograms. Next, we estimated source-ray parameters (source p values) and receiver-ray parameters (receiver p values) for the peaks and troughs above a threshold amplitude in separated P- and S-wavefields. For each PP and PS reflection, we traced (1) a source ray in the P-velocity model in the direction of the emitted ray angle (determined by the source p value) and (2) a receiver ray in the P- or S-velocity model back in the direction of the emergent PP- or PS-wave ray angle (determined by the PP- or PS-wave receiver p value), respectively. The image-point position was adjusted from the intersection of the source and receiver rays to the point where the sum of the source time and receiver-ray time equaled the two-way traveltime. The orientation of the reflector surface was determined to satisfy Snell’s law at the intersection point. The amplitude of a P-wave (or an S-wave) was distributed over the first Fresnel zone along the reflector surface in the P- (or S-) image. Stacking over all P-images of the PP-wave common-source gathers gave the stacked P-image, and stacking over all S-images of the PS-wave common-source gathers gave the stacked S-image. Synthetic examples showed acceptable migration quality; however, the images were less complete than those produced by scalar reverse-time migration (RTM). The computing time for the 2D examples used was about 1/30 of that for scalar RTM of the same data.

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