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.

Time-lapse amplitude variation with offset inversion using Bayesian theory in two-phase media

Geophysics ◽  
2019 ◽  
Vol 84 (3) ◽  
pp. N55-N79
Author(s):  
Longxiao Zhi ◽  
Hanming Gu
Keyword(s):  
Objective Function ◽  
Physical Model ◽  
Time Lapse ◽  
Bayesian Theory ◽  
Amplitude Variation ◽  
Two Phase ◽  
Amplitude Variation With Offset ◽  
Zoeppritz Equations ◽  
Avo Inversion ◽  
Rock Physical Model

In time-lapse seismic analysis, the Zoeppritz equations are usually used in the time-lapse amplitude variation with offset (AVO) inversion and then combined with a rock-physical model to estimate the reservoir-parameter changes. The real-life reservoir is a two-phase medium that consists of solid and fluid components. The Zoeppritz equations are a simplification, assuming a single-phase solid medium, in which the properties of this medium are estimated by effective parameters from the combined components. This means that the Zoeppritz equations cannot describe the characteristics of the seismic reflection amplitudes in the reservoir in an accurate way. Therefore, we develop a method for time-lapse AVO inversion in two-phase media using the Bayesian theory to estimate the reservoir parameters and their changes quantitatively. We use a reflection-coefficient equation in two-phase media, a rock-physical model, and the convolutional model to build a relationship between the seismic records and reservoir parameters, which include porosity, clay content, saturation, and pressure. Assuming that the seismic-data errors follow a zero-mean Gaussian distribution and that the reservoir parameters follow a four-variable Cauchy prior distribution, we use the Bayesian theory to construct the objective function for the AVO inversion, and we also add a model-constraint term to compensate the low-frequency information and improve the stability of the inversion. Using the objective function of the AVO inversion and the Gauss-Newton method, we derived the equation for time-lapse AVO inversion. This result can be used to estimate the reservoir parameters and their changes accurately and in a stable way. The test results from the feasibility study on synthetic and field data proved that the method is effective and reliable.

Estimating saturation and density changes caused by CO2 injection at Sleipner — Using time-lapse seismic amplitude-variation-with-offset and time-lapse gravity

2017 ◽  
Vol 5 (2) ◽  
pp. T243-T257 ◽  
Author(s):  
Martin Landrø ◽  
Mark Zumberge
Keyword(s):  
Gravity Data ◽  
Time Lapse ◽  
Co2 Injection ◽  
Injection Point ◽  
Amplitude Variation With Offset ◽  
Seismic Method ◽  
Seismic Amplitude ◽  
Gravity Measurements ◽  
Measured Time ◽  
Best Fit

We have developed a calibrated, simple time-lapse seismic method for estimating saturation changes from the [Formula: see text]-storage project at Sleipner offshore Norway. This seismic method works well to map changes when [Formula: see text] is migrating laterally away from the injection point. However, it is challenging to detect changes occurring below [Formula: see text] layers that have already been charged by some [Formula: see text]. Not only is this partly caused by the seismic shadow effects, but also by the fact that the velocity sensitivity for [Formula: see text] change in saturation from 0.3 to 1.0 is significantly less than saturation changes from zero to 0.3. To circumvent the seismic shadow zone problem, we combine the time-lapse seismic method with time-lapse gravity measurements. This is done by a simple forward modeling of gravity changes based on the seismically derived saturation changes, letting these saturation changes be scaled by an arbitrary constant and then by minimizing the least-squares error to obtain the best fit between the scaled saturation changes and the measured time-lapse gravity data. In this way, we are able to exploit the complementary properties of time-lapse seismic and gravity data.

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.

Linearized amplitude variation with offset (AVO) inversion with supercritical angles

Geophysics ◽  
2006 ◽  
Vol 71 (5) ◽  
pp. E49-E55 ◽  
Author(s):  
Jonathan E. Downton ◽  
Charles Ursenbach
Keyword(s):  
Reflection Coefficient ◽  
Critical Angle ◽  
Waveform Inversion ◽  
Phase Variation ◽  
Reflection Coefficients ◽  
Amplitude Variation ◽  
Amplitude Variation With Offset ◽  
Zoeppritz Equations ◽  
Avo Inversion ◽  
Angle Data

Contrary to popular belief, a linearized approximation of the Zoeppritz equations may be used to estimate the reflection coefficient for angles of incidence up to and beyond the critical angle. These supercritical reflection coefficients are complex, implying a phase variation with offset in addition to amplitude variation with offset (AVO). This linearized approximation is then used as the basis for an AVO waveform inversion. By incorporating this new approximation, wider offset and angle data may be incorporated in the AVO inversion, helping to stabilize the problem and leading to more accurate estimates of reflectivity, including density reflectivity.

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.

Effects of vertically aligned subsurface fractures on seismic reflections: A physical model study

Geophysics ◽  
1997 ◽  
Vol 62 (1) ◽  
pp. 245-252 ◽  
Author(s):  
Chih‐Hsiung Chang ◽  
Gerald H. F. Gardner
Keyword(s):  
Physical Model ◽  
Transverse Direction ◽  
Amplitude Variation ◽  
Experimental Conditions ◽  
Amplitude Variation With Offset ◽  
Model Study ◽  
Strike Direction ◽  
Vertically Aligned ◽  
Subsurface Fractures ◽  
Seismic Reflections

We investigate the effects of subsurface fractures on moveout velocity and on reflection amplitudes by constructing a fractured model with three layers. The physical model was constructed by embedding a Phenolitic disc within the intermediate layer, which acts as a zone of vertical fractures. Survey lines were run along seven azimuthal directions between the strike direction and the transverse direction to the fractures at an angle increment of 15°. For our set of experimental conditions, we observe that the horizontal moveout velocity decreases from the strike direction toward the transverse direction to the fractures, and that the rate of decrease in amplitude variation with offset (AVO) increases from the strike direction toward the transverse direction to the fracture.

scholarly journals Estimation and accounting for the modeling error in probabilistic linearized amplitude variation with offset inversion

Geophysics ◽  
2018 ◽  
Vol 83 (2) ◽  
pp. N15-N30 ◽  
Author(s):  
Rasmus Bødker Madsen ◽  
Thomas Mejer Hansen
Keyword(s):  
Probability Distribution ◽  
Signal To Noise Ratio ◽  
Noise Model ◽  
Modeling Error ◽  
Amplitude Variation ◽  
Amplitude Variation With Offset ◽  
Zoeppritz Equations ◽  
Avo Inversion ◽  
Convolution Model ◽  
Modeling Errors

A linearized form of Zoeppritz equations combined with the convolution model is widely used in inversion of amplitude variation with offset (AVO) seismic data. This is shown to introduce a “modeling error,” compared with using the full Zoeppritz equations, whose magnitude depends on the degree of subsurface heterogeneity. Then, we evaluate a methodology for quantifying this modeling error through a probability distribution. First, a sample of the unknown probability density describing the modeling error is generated. Then, we determine how this sample can be described by a correlated Gaussian probability distribution. Finally, we develop how such modeling errors affect the linearized AVO inversion results. If not accounted for (which is most often the case), the modeling errors can introduce significant artifacts in the inversion results, if the signal-to-noise ratio is less than 2, as is the case for most AVO data obtained today. However, if accounted for, such artifacts can be avoided. The methodology can easily be adapted and applied to most linear AVO inversion methods, by allowing the use of the inferred modeling error as a correlated Gaussian noise model.

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