High-Order AVO Inversion for Effective Pore-Fluid Bulk Modulus Based on Series Reversion and Bayesian Theory

Pore-fluid identification is one of the key technologies in seismic exploration. Fluid indicators play important roles in pore-fluid identification. For sandstone reservoirs, the effective pore-fluid bulk modulus is more susceptible to pore-fluid than other fluid indicators. AVO (amplitude variation with offset) inversion is an effective way to obtain fluid indicators from seismic data directly. Nevertheless, current methods lack a high-order AVO equation for a direct, effective pore-fluid bulk modulus inversion. Therefore, based on the Zoeppritz equations and Biot–Gassmann theory, we derived a high-order P-wave AVO approximation for an effective pore-fluid bulk modulus. Series reversion and Bayesian theory were introduced to establish a direct non-linear P-wave AVO inversion method. By adopting this method, the effective pore-fluid bulk modulus, porosity, and density can be inverted directly from seismic data. Numerical simulation results demonstrate the precision of our proposed method. Model and field data evaluations show that our method is stable and feasible.

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Computation of dry-rock VP/VS ratio, fluid property factor, and density estimation from amplitude-variation-with-offset inversion

Geophysics ◽

10.1190/geo2017-0621.1 ◽

2018 ◽

Vol 83 (6) ◽

pp. R669-R679 ◽

Cited By ~ 1

Author(s):

Gang Chen ◽

Xiaojun Wang ◽

Baocheng Wu ◽

Hongyan Qi ◽

Muming Xia

Keyword(s):

Reservoir Characterization ◽

Synthetic Data ◽

Pore Fluid ◽

Inversion Method ◽

S Wave ◽

Amplitude Variation ◽

Amplitude Variation With Offset ◽

Fluid Identification ◽

Fluid Property ◽

Avo Inversion

Estimating the fluid property factor and density from amplitude-variation-with-offset (AVO) inversion is important for fluid identification and reservoir characterization. The fluid property factor can distinguish pore fluid in the reservoir and the density estimate aids in evaluating reservoir characteristics. However, if the scaling factor of the fluid property factor (the dry-rock [Formula: see text] ratio) is chosen inappropriately, the fluid property factor is not only related to the pore fluid, but it also contains a contribution from the rock skeleton. On the other hand, even if the angle gathers include large angles (offsets), a three-parameter AVO inversion struggles to estimate an accurate density term without additional constraints. Thus, we have developed an equation to compute the dry-rock [Formula: see text] ratio using only the P- and S-wave velocities and density of the saturated rock from well-logging data. This decouples the fluid property factor from lithology. We also developed a new inversion method to estimate the fluid property factor and density parameters, which takes full advantage of the high stability of a two-parameter AVO inversion. By testing on a portion of the Marmousi 2 model, we find that the fluid property factor calculated by the dry-rock [Formula: see text] ratio obtained by our method relates to the pore-fluid property. Simultaneously, we test the AVO inversion method for estimating the fluid property factor and density parameters on synthetic data and analyze the feasibility and stability of the inversion. A field-data example indicates that the fluid property factor obtained by our method distinguishes the oil-charged sand channels and the water-wet sand channel from the well logs.

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Fluid Discrimination Based on Frequency-Dependent AVO Inversion with the Elastic Parameter Sensitivity Analysis

Geofluids ◽

10.1155/2019/8750127 ◽

2019 ◽

Vol 2019 ◽

pp. 1-13

Author(s):

Pu Wang ◽

Jingye Li ◽

Xiaohong Chen ◽

Kedong Wang ◽

Benfeng Wang

Keyword(s):

Data Interpretation ◽

Elastic Parameter ◽

Pore Fluid ◽

P Wave ◽

Frequency Dependent ◽

Avo Inversion ◽

Fluid Factor ◽

Dispersion Terms ◽

Dispersion And Attenuation ◽

Dispersion Term

Fluid discrimination is an extremely important part of seismic data interpretation. It plays an important role in the refined description of hydrocarbon-bearing reservoirs. The conventional AVO inversion based on Zoeppritz’s equation shows potential in lithology prediction and fluid discrimination; however, the dispersion and attenuation induced by pore fluid are not fully considered. The relationship between dispersion terms in different frequency-dependent AVO equations has not yet been discussed. Following the arguments of Chapman, the influence of pore fluid on elastic parameters is analyzed in detail. We find that the dispersion and attenuation of Russell fluid factor, Lamé parameter, and bulk modulus are more pronounced than those of P-wave modulus. The Russell fluid factor is most prominent among them. Based on frequency-dependent AVO inversion, the uniform expression of different dispersion terms of these parameters is derived. Then, incorporating the P-wave difference with the dispersion terms, we obtain new P-wave difference dispersion factors which can identify the gas-bearing reservoir location better compared with the dispersion terms. Field data application also shows that the dispersion term of Russell fluid factor is optimal in identifying fluid. However, the dispersion term of Russell fluid factor could be unsatisfactory, if the value of the weighting parameter associated with dry rock is improper. Then, this parameter is studied to propose a reasonable setting range. The results given by this paper are helpful for the fluid discrimination in hydrocarbon-bearing rocks.

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Two-step joint PP- and PS-wave three-term amplitude-variation with offset inversion

Interpretation ◽

10.1190/int-2016-0056.1 ◽

2016 ◽

Vol 4 (4) ◽

pp. T613-T625 ◽

Cited By ~ 1

Author(s):

Qizhen Du ◽

Bo Zhang ◽

Xianjun Meng ◽

Chengfeng Guo ◽

Gang Chen ◽

...

Keyword(s):

Reflection Coefficient ◽

Wave Reflection ◽

Coefficient Matrix ◽

P Wave ◽

Inversion Method ◽

S Wave ◽

Amplitude Variation ◽

Amplitude Variation With Offset ◽

Avo Inversion ◽

Pp Wave

Three-term amplitude-variation with offset (AVO) inversion generally suffers from instability when there is limited prior geologic or petrophysical constraints. Two-term AVO inversion shows higher instability compared with three-term AVO inversion. However, density, which is important in the fluid-type estimation, cannot be recovered from two-term AVO inversion. To reliably predict the P- and S-waves and density, we have developed a robust two-step joint PP- and PS-wave three-term AVO-inversion method. Our inversion workflow consists of two steps. The first step is to estimate the P- and S-wave reﬂectivities using Stewart’s joint two-term PP- and PS-AVO inversion. The second step is to treat the P-wave reflectivity obtained from the first step as the prior constraint to remove the P-wave velocity related-term from the three-term Aki-Richards PP-wave approximated reflection coefficient equation, and then the reduced PP-wave reflection coefficient equation is combined with the PS-wave reflection coefficient equation to estimate the S-wave and density reﬂectivities. We determined the effectiveness of our method by first applying it to synthetic models and then to field data. We also analyzed the condition number of the coefficient matrix to illustrate the stability of the proposed method. The estimated results using proposed method are superior to those obtained from three-term AVO inversion.

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Simultaneous inversion of prestack seismic data for rock properties using simulated annealing

Geophysics ◽

10.1190/1.1527087 ◽

2002 ◽

Vol 67 (6) ◽

pp. 1877-1885 ◽

Cited By ~ 85

Author(s):

Xin‐Quan Ma

Keyword(s):

Simulated Annealing ◽

Seismic Data ◽

Low Frequency ◽

Optimization Procedure ◽

P Wave ◽

Rock Properties ◽

Inversion Method ◽

Inversion Algorithm ◽

Simultaneous Inversion ◽

Reflection Seismic

A new prestack inversion algorithm has been developed to simultaneously estimate acoustic and shear impedances from P‐wave reflection seismic data. The algorithm uses a global optimization procedure in the form of simulated annealing. The goal of optimization is to find a global minimum of the objective function, which includes the misfit between synthetic and observed prestack seismic data. During the iterative inversion process, the acoustic and shear impedance models are randomly perturbed, and the synthetic seismic data are calculated and compared with the observed seismic data. To increase stability, constraints have been built into the inversion algorithm, using the low‐frequency impedance and background Vs/Vp models. The inversion method has been successfully applied to synthetic and field data examples to produce acoustic and shear impedances comparable to log data of similar bandwidth. The estimated acoustic and shear impedances can be combined to derive other elastic parameters, which may be used for identifying of lithology and fluid content of reservoirs.

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Simultaneous inversion of PP and PS seismic data

Geophysics ◽

10.1190/1.2194533 ◽

2006 ◽

Vol 71 (3) ◽

pp. R1-R10 ◽

Cited By ~ 51

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.

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Constrained nonlinear amplitude variation with offset inversion using Zoeppritz equations

Geophysics ◽

10.1190/geo2017-0543.1 ◽

2018 ◽

Vol 83 (3) ◽

pp. R245-R255 ◽

Cited By ~ 8

Author(s):

Ali Gholami ◽

Hossein S. Aghamiry ◽

Mostafa Abbasi

Keyword(s):

Nonlinear Equations ◽

Wave Velocity ◽

Seismic Data ◽

P Wave ◽

Simultaneous Estimation ◽

Model Parameters ◽

Amplitude Variation ◽

Amplitude Variation With Offset ◽

Zoeppritz Equations ◽

Avo Inversion

The inversion of prestack seismic data using amplitude variation with offset (AVO) has received increased attention in the past few decades because of its key role in estimating reservoir properties. AVO is mainly governed by the Zoeppritz equations, but traditional inversion techniques are based on various linear or quasilinear approximations to these nonlinear equations. We have developed an efficient algorithm for nonlinear AVO inversion of precritical reflections using the exact Zoeppritz equations in multichannel and multi-interface form for simultaneous estimation of the P-wave velocity, S-wave velocity, and density. The total variation constraint is used to overcome the ill-posedness while solving the forward nonlinear model and to preserve the sharpness of the interfaces in the parameter space. The optimization is based on a combination of Levenberg’s algorithm and the split Bregman iterative scheme, in which we have to refine the data and model parameters at each iteration. We refine the data via the original nonlinear equations, but we use the traditional cost-effective linearized AVO inversion to construct the Jacobian matrix and update the model. Numerical experiments show that this new iterative procedure is convergent and converges to a solution of the nonlinear problem. We determine the performance and optimality of our nonlinear inversion algorithm with various simulated and field seismic data sets.

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An Improved Scheme of Frequency-Dependent AVO Inversion Method and Its Application for Tight Gas Reservoirs

Geofluids ◽

10.1155/2019/3525818 ◽

2019 ◽

Vol 2019 ◽

pp. 1-12 ◽

Cited By ~ 2

Author(s):

Jiong Liu ◽

Jun-rui Ning ◽

Xi-wu Liu ◽

Chun-yuan Liu ◽

Tian-sheng Chen

Keyword(s):

Seismic Velocity ◽

Seismic Exploration ◽

Inversion Method ◽

High Dispersion ◽

Tight Gas ◽

Gas Reservoirs ◽

Frequency Dependent ◽

Tight Gas Reservoirs ◽

Avo Inversion ◽

Gas Bearing

AVO inversion is a seismic exploration methodology used to predict the earth’s elastic parameters and thus rocks and fluid properties. It is built up on elastic theory and does not consider the seismic dispersion in real strata. Recent experiments and theory of rock physics have shown that in hydrocarbon-bearing rocks, especially in gas-bearing ones, the change of seismic velocity with frequency may be pretty remarkable for fluid flow in pore space. Some scholars proposed methods about seismic dispersion, such as frequency-dependent AVO inversion, to forecast oil and gas reservoirs underground. In this paper, we demonstrate an improved scheme of frequency-dependent AVO inversion, which is based on conventional Smith-Gidlow’s AVO equation, to extract seismic dispersion and predict the hydrocarbon underground. A simple model with gas-bearing reservoir is devised to validate the inversion scheme, and further analysis indicates that our scheme is more accurate and reasonable than the previous scheme. Our new scheme applied to the tight gas reservoirs in Fenggu area of western Sichuan depression of China finds that regions with high dispersion gradients correlate well with regions with prolific gas. Analysis and case studies show that our scheme of frequency-dependent AVO inversion is an efficient approach to predict gas reservoirs underground.

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Joint anisotropic amplitude variation with offset inversion of PP and PS seismic data

Geophysics ◽

10.1190/geo2016-0516.1 ◽

2018 ◽

Vol 83 (2) ◽

pp. N31-N50 ◽

Cited By ~ 8

Author(s):

Jun Lu ◽

Yun Wang ◽

Jingyi Chen ◽

Ying An

Keyword(s):

Seismic Data ◽

Local Minimum ◽

Synthetic Data ◽

Inversion Method ◽

Amplitude Variation ◽

Amplitude Variation With Offset ◽

Avo Inversion ◽

Time Migration ◽

Amplitude Variation With Angle ◽

Two Stages

With the increase in exploration target complexity, more parameters are required to describe subsurface properties, particularly for finely stratified reservoirs with vertical transverse isotropic (VTI) features. We have developed an anisotropic amplitude variation with offset (AVO) inversion method using joint PP and PS seismic data for VTI media. Dealing with local minimum solutions is critical when using anisotropic AVO inversion because more parameters are expected to be derived. To enhance the inversion results, we adopt a hierarchical inversion strategy to solve the local minimum solution problem in the Gauss-Newton method. We perform the isotropic and anisotropic AVO inversions in two stages; however, we only use the inversion results from the first stage to form search windows for constraining the inversion in the second stage. To improve the efficiency of our method, we built stop conditions using Euclidean distance similarities to control iteration of the anisotropic AVO inversion in noisy situations. In addition, we evaluate a time-aligned amplitude variation with angle gather generation approach for our anisotropic AVO inversion using anisotropic prestack time migration. We test the proposed method on synthetic data in ideal and noisy situations, and find that the anisotropic AVO inversion method yields reasonable inversion results. Moreover, we apply our method to field data to show that it can be used to successfully identify complex lithologic and fluid information regarding fine layers in reservoirs.

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Bayesian linearized AVO inversion

Geophysics ◽

10.1190/1.1543206 ◽

2003 ◽

Vol 68 (1) ◽

pp. 185-198 ◽

Cited By ~ 450

Author(s):

Arild Buland ◽

Henning Omre

Keyword(s):

Wave Velocity ◽

Posterior Distribution ◽

Acoustic Impedance ◽

Velocity Ratio ◽

P Wave ◽

Inversion Method ◽

Inversion Algorithm ◽

S Wave ◽

Data Set ◽

Avo Inversion

A new linearized AVO inversion technique is developed in a Bayesian framework. The objective is to obtain posterior distributions for P‐wave velocity, S‐wave velocity, and density. Distributions for other elastic parameters can also be assessed—for example, acoustic impedance, shear impedance, and P‐wave to S‐wave velocity ratio. The inversion algorithm is based on the convolutional model and a linearized weak contrast approximation of the Zoeppritz equation. The solution is represented by a Gaussian posterior distribution with explicit expressions for the posterior expectation and covariance; hence, exact prediction intervals for the inverted parameters can be computed under the specified model. The explicit analytical form of the posterior distribution provides a computationally fast inversion method. Tests on synthetic data show that all inverted parameters were almost perfectly retrieved when the noise approached zero. With realistic noise levels, acoustic impedance was the best determined parameter, while the inversion provided practically no information about the density. The inversion algorithm has also been tested on a real 3‐D data set from the Sleipner field. The results show good agreement with well logs, but the uncertainty is high.

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AVO inversion and poroelasticity with P- and S-wave moduli

Geophysics ◽

10.1190/geo2011-0214.1 ◽

2012 ◽

Vol 77 (6) ◽

pp. N17-N24 ◽

Cited By ~ 76

Author(s):

Zhaoyun Zong ◽

Xingyao Yin ◽

Guochen Wu

Keyword(s):

Seismic Data ◽

Real Data ◽

P Wave ◽

Model Parameters ◽

Initial Model ◽

S Wave ◽

Data Set ◽

Statistical Probability ◽

Avo Inversion ◽

Poroelasticity Theory

The fluid term in the Biot-Gassmann equation plays an important role in reservoir fluid discrimination. The density term imbedded in the fluid term, however, is difficult to estimate because it is less sensitive to seismic amplitude variations. We combined poroelasticity theory, amplitude variation with offset (AVO) inversion, and identification of P- and S-wave moduli to present a stable and physically meaningful method to estimate the fluid term, with no need for density information from prestack seismic data. We used poroelasticity theory to express the fluid term as a function of P- and S-wave moduli. The use of P- and S-wave moduli made the derivation physically meaningful and natural. Then we derived an AVO approximation in terms of these moduli, which can then be directly inverted from seismic data. Furthermore, this practical and robust AVO-inversion technique was developed in a Bayesian framework. The objective was to obtain the maximum a posteriori solution for the P-wave modulus, S-wave modulus, and density. Gaussian and Cauchy distributions were used for the likelihood and a priori probability distributions, respectively. The introduction of a low-frequency constraint and statistical probability information to the objective function rendered the inversion more stable and less sensitive to the initial model. Tests on synthetic data showed that all the parameters can be estimated well when no noise is present and the estimated P- and S-wave moduli were still reasonable with moderate noise and rather smooth initial model parameters. A test on a real data set showed that the estimated fluid term was in good agreement with the results of drilling.

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