Information on elastic parameters obtained from the amplitudes of reflected waves

Geophysics ◽  
1995 ◽  
Vol 60 (5) ◽  
pp. 1426-1436 ◽  
Author(s):  
Wojciech Dȩbski ◽  
Albert Tarantola
Keyword(s):  
Mass Density ◽  
Seismic Velocities ◽  
Elastic Parameters ◽  
Amplitude Variation ◽  
Amplitude Variation With Offset ◽  
Reflected Waves ◽  
Seismic Amplitude ◽  
Earth Models ◽  
Definition Of ◽  
Proper Analysis

Seismic amplitude variation with offset data contain information on the elastic parameters of geological layers. As the general solution of the inverse problem consists of a probability over the space of all possible earth models, we look at the probabilities obtained using amplitude variation with offset (AVO) data for different choices of elastic parameters. A proper analysis of the information in the data requires a nontrivial definition of the probability defining the state of total ignorance on different elastic parameters (seismic velocities, Lamé’s parameters, etc.). We conclude that mass density, seismic impedance, and Poisson’s ratio constitute the best resolved parameter set when inverting seismic amplitude variation with offset data.

Meet West Africa deep exploration challenge with geomorphology and targeted amplitude variation with offset inversion

2020 ◽  
Vol 8 (1) ◽  
pp. SA25-SA33
Author(s):  
Ellen Xiaoxia Xu ◽  
Yu Jin ◽  
Sarah Coyle ◽  
Dileep Tiwary ◽  
Henry Posamentier ◽  
...  
Keyword(s):  
West Africa ◽  
Critical Role ◽  
Reservoir Quality ◽  
Quality Prediction ◽  
Reservoir Prediction ◽  
Amplitude Variation ◽  
Amplitude Variation With Offset ◽  
Simultaneous Inversion ◽  
Seismic Amplitude ◽  
Class 1

Seismic amplitude has played a critical role in the exploration and exploitation of hydrocarbon in West Africa. Class 3 and 2 amplitude variation with offset (AVO) was extensively used as a direct hydrocarbon indicator and reservoir prediction tool in Neogene assets. As exploration advanced to deeper targets with class 1 AVO seismic character, the usage of seismic amplitude for reservoir presence and quality prediction became challenged. To overcome this obstacle, (1) we used seismic geomorphology to infer reservoir presence and precisely target geophysical analysis on reservoir prone intervals, (2) we applied rigorous prestack data preparation to ensure the accuracy and precision of AVO simultaneous inversion for reservoir quality prediction, and (3) we used lateral statistic method to sum up AVO behavior in regions of contrasts to infer reservoir quality changes. We have evaluated a case study in which the use of the above three techniques resulted in confident prediction of reservoir presence and quality. Our results reduced the uncertainty around the biggest risk element in reservoir among the source, charge, and trap mechanism in the prospecting area. This work ultimately made a significant contribution toward a confident resource booking.

scholarly journals An Accurate Jacobian Matrix with Exact Zoeppritz for Elastic Moduli of Dry Rock

2019 ◽  
Vol 9 (24) ◽  
pp. 5485
Author(s):  
Xiaobo Liu ◽  
Jingyi Chen ◽  
Fuping Liu ◽  
Zhencong Zhao
Keyword(s):  
Jacobian Matrix ◽  
Incidence Angle ◽  
Inversion Method ◽  
Seismic Velocities ◽  
Amplitude Variation ◽  
Amplitude Variation With Offset ◽  
Shear Moduli ◽  
Partial Derivatives ◽  
Solid Matrices ◽  
Derivatives Of

Seismic velocities are related to the solid matrices and the pore fluids. The bulk and shear moduli of dry rock are the primary parameters to characterize solid matrices. Amplitude variation with offset (AVO) or amplitude variation with incidence angle (AVA) is the most used inversion method to discriminate lithology in hydrocarbon reservoirs. The bulk and shear moduli of dry rock, however, cannot be inverted directly using seismic data and the conventional AVO/AVA inversions. The most important step to accurately invert these dry rock parameters is to derive the Jacobian matrix. The combination of exact Zoeppritz and Biot–Gassmann equations makes it possible to directly calculate the partial derivatives of seismic reflectivities (PP-and PS-waves) with respect to dry rock moduli. During this research, we successfully derive the accurate partial derivatives of the exact Zoeppritz equations with respect to bulk and shear moduli of dry rock. The characteristics of these partial derivatives are investigated in the numerical examples. Additionally, we compare the partial derivatives using this proposed algorithm with the classical Shuey and Aki–Richards approximations. The results show that this derived Jacobian matrix is more accurate and versatile. It can be used further in the conventional AVO/AVA inversions to invert bulk and shear moduli of dry rock directly.

Value of information analysis and Bayesian inversion for closed skew-normal distributions: Applications to seismic amplitude variation with offset data

Geophysics ◽  
2014 ◽  
Vol 79 (4) ◽  
pp. R151-R163 ◽  
Author(s):  
Javad Rezaie ◽  
Jo Eidsvik ◽  
Tapan Mukerji
Keyword(s):  
Seismic Data ◽  
Value Of Information ◽  
Bayesian Inversion ◽  
Information Analysis ◽  
Amplitude Variation ◽  
Mean Values ◽  
Amplitude Variation With Offset ◽  
Seismic Amplitude ◽  
Value Of Information Analysis ◽  
Skew Normal

Information analysis can be used in the context of reservoir decisions under uncertainty to evaluate whether additional data (e.g., seismic data) are likely to be useful in impacting the decision. Such evaluation of geophysical information sources depends on input modeling assumptions. We studied results for Bayesian inversion and value of information analysis when the input distributions are skewed and non-Gaussian. Reservoir parameters and seismic amplitudes are often skewed and using models that capture the skewness of distributions, the input assumptions are less restrictive and the results are more reliable. We examined the general methodology for value of information analysis using closed skew normal (SN) distributions. As an example, we found a numerical case with porosity and saturation as reservoir variables and computed the value of information for seismic amplitude variation with offset intercept and gradient, all modeled with closed SN distributions. Sensitivity of the value of information analysis to skewness, mean values, accuracy, and correlation parameters is performed. Simulation results showed that fewer degrees of freedom in the reservoir model results in higher value of information, and seismic data are less valuable when seismic measurements are spatially correlated. In our test, the value of information was approximately eight times larger for a spatial-dependent reservoir variable compared with the independent case.

The O’Doherty‐Anstey formula and localization of seismic waves

Geophysics ◽  
1993 ◽  
Vol 58 (5) ◽  
pp. 736-740 ◽  
Author(s):  
Serguei A. Shapiro ◽  
Holger Zien
Keyword(s):  
Multiple Scattering ◽  
Seismic Waves ◽  
Seismic Attenuation ◽  
Adequate Description ◽  
Amplitude Variation ◽  
Amplitude Variation With Offset ◽  
Seismic Profiling ◽  
Seismic Amplitude ◽  
Vertical Seismic Profiling ◽  
Vsp Data

Angle (or offset) dependent effects of scattering in finely layered media can be observed and analyzed or must be compensated for in vertical seismic profiling data (VSP‐ data), crosshole observations, or seismic amplitude variation with offset (AVO) measurements. Moreover, the adequate description of multiple scattering is important for the study of seismic attenuation in sediments and for the design of inversion procedures.

scholarly journals AVO as a fluid indicator: A physical modeling study

Geophysics ◽  
2007 ◽  
Vol 72 (1) ◽  
pp. C9-C17 ◽  
Author(s):  
Aaron Wandler ◽  
Brian Evans ◽  
Curtis Link
Keyword(s):  
Physical Model ◽  
Physical Modeling ◽  
Model Experiment ◽  
Close Agreement ◽  
Time Lapse ◽  
Amplitude Variation ◽  
Amplitude Variation With Offset ◽  
Zoeppritz Equations ◽  
Seismic Amplitude ◽  
Gradient Domain

Information on time-lapse changes in seismic amplitude variation with offset (AVO) from a reservoir can be used to optimize production. We designed a scaled physical model experiment to study the AVO response of mixtures of brine, oil, and carbon dioxide at pressures of 0, 1.03, and [Formula: see text]. The small changes in density and velocity for each fluid because of increasing pressure were not detectable and were assumed to lie within the error of the experiment. However, AVO analysis was able to detect changes in the elastic properties between fluids that contained oil and those that did not. When the AVO response was plotted in the AVO intercept-gradient domain, fluids containing oil were clearly separated from fluids not containing oil. This was observed in the AVO response from both the top and base of the fluids in the physical model. We then compared the measured AVO response with the theoretical AVO response given by the Zoeppritz equations. The measured and theoretical AVO intercept responses for the top fluid reflection agree well, although the AVO gradients disagree slightly. For the fluid base reflection, the measured and theoretical responses are in close agreement.

Some effects of velocity variation on AVO and its interpretation

Geophysics ◽  
1993 ◽  
Vol 58 (9) ◽  
pp. 1297-1300 ◽  
Author(s):  
Yu Xu ◽  
G. H. F. Gardner ◽  
J. A. McDonald
Keyword(s):  
Velocity Gradient ◽  
Poisson’S Ratio ◽  
Incident Angle ◽  
Elastic Parameter ◽  
Poisson's Ratio ◽  
Velocity Variation ◽  
Elastic Parameters ◽  
Amplitude Variation ◽  
Amplitude Variation With Offset ◽  
Meaningful Relationship

In recent years interest has increased in the interpretation of the amplitude variation of reflected signals as a function of offset (AVO). A more meaningful relationship for interpreting reflection coefficients at the target horizon is amplitude variation with incident angle (AVA). The challenge is to convert from AVO to AVA. The effects of velocity variation in the overburden on amplitude variation with offset (AVO) and on the final inversion of AVO data into velocity, density, and Poisson’s ratio can be significant. Examples are given here for subsurface medium with a vertical velocity gradient range of [Formula: see text] to [Formula: see text]. When the medium is treated as homogeneous in the conversion from AVO to AVA, this velocity variation causes significant errors (about 10 percent) in both the gradient of AVA and in the normal incident reflection coefficient. Such errors produce errors of similar magnitude in the inversion of AVA data into the elastic parameters of velocity, Poisson’s ratio, and density. The errors depend on the velocity gradient, the offset range, the elastic parameter contrast across the interface, and the interface depth.

Effect of VS predictors on amplitude variation with offset response

Geophysics ◽  
2018 ◽  
Vol 83 (1) ◽  
pp. N1-N13
Author(s):  
Humberto S. Arévalo-López ◽  
Uri Wollner ◽  
Jack P. Dvorkin
Keyword(s):  
Seismic Data ◽  
Poor Quality ◽  
Rock Properties ◽  
Specific Situation ◽  
S Wave ◽  
Amplitude Variation ◽  
Amplitude Variation With Offset ◽  
Seismic Amplitude ◽  
Text Prediction ◽  
A Site

We have posed a question whether the differences between various [Formula: see text] predictors affect one of the ultimate goals of [Formula: see text] prediction, generating synthetic amplitude variation with offset (AVO) gathers to serve as a calibration tool for interpreting the seismic amplitude for rock properties and conditions. We address this question by evaluating examples in which we test several such predictors at an interface between two elastic layers, at pseudowells, and at a real well with poor-quality S-wave velocity data. The answer based on the examples presented is that no matter which [Formula: see text] predictor is used, the seismic responses at a reservoir are qualitatively identical. The choice of a [Formula: see text] predictor does not affect our ability (or inability) to forecast the presence of hydrocarbons from seismic data. We also find that the amplitude versus angle responses due to different predictors consistently vary along the same pattern, no matter which predictor is used. Because our analysis uses a “by-example” approach, the conclusions are not entirely general. However, the method of comparing the AVO responses due to different [Formula: see text] predictors discussed here is. Hence, in a site-specific situation, we recommend using several relevant predictors to ascertain whether the choice significantly affects the synthetic AVO response and if this response is consistent with veritable seismic data.

A data-driven amplitude variation with offset inversion method via learned dictionaries and sparse representation

Geophysics ◽  
2018 ◽  
Vol 83 (6) ◽  
pp. R725-R748 ◽  
Author(s):  
Bin She ◽  
Yaojun Wang ◽  
Jiandong Liang ◽  
Zhining Liu ◽  
Chengyun Song ◽  
...  
Keyword(s):  
Sparse Representation ◽  
Prior Information ◽  
Data Driven ◽  
Inversion Method ◽  
Model Parameters ◽  
Well Log ◽  
Elastic Parameters ◽  
Amplitude Variation ◽  
Amplitude Variation With Offset ◽  
Avo Inversion

Amplitude variation with offset (AVO) inversion is a typical ill-posed inverse problem. To obtain a stable and unique solution, regularization techniques relying on mathematical models from prior information are commonly used in conventional AVO inversion methods (hence the name model-driven methods). Due to the difference between prior information and the actual geology, these methods often have difficulty achieving satisfactory accuracy and resolution. We have developed a novel data-driven inversion method for the AVO inversion problem. This method can effectively extract useful knowledge from well-log data, including sparse dictionaries of elastic parameters and sparse representation of subsurface model parameters. Lateral continuity of subsurface geology allows for the approximation of model parameters for a work area using the learned dictionaries. Instead of particular mathematical models, a sparse representation is used to constrain the inverse problem. Because no assumption is made about the model parameters, we consider this a data-driven method. The general process of the algorithm is as follows: (1) using well-log data as the training samples to learn the sparse dictionary of each elastic parameter, (2) imposing a sparse representation constraint on the objective function, making the elastic parameters be sparsely represented over the learned dictionary, and (3) solving the objective function by applying a coordinate-descent algorithm. Tests on several synthetic examples and field data demonstrate that our algorithm is effective in improving the resolution and accuracy of solutions and is adaptable to various geologies.

Sign in / Sign up

Export Citation Format

Share Document