Blocky inversion of time‐lapse seismic AVO data

Eidsvik, Jo; Theune, Ulrich

doi:10.1190/1.3255664

Cited by 3 publications

(1 citation statement)

References 3 publications

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“…When using Bayesian theory to construct the inversion objective function, the accuracy and resolution of the inversion results can be further improved by introducing a more reasonable priori model. Eidsvik and Theune (2009) studied blocky inversion method for time-lapse seismic AVO data by introducing a blockiness constrained prior model, and the actual data processing results demonstrated that this method could better reflect the changes of time-lapse elastic parameters. Theune et al (2010) pointed out that the vertical blockiness constraint, obeying the differentiable Laplace distribution, could more reliably capture sharp time-lapse changes in elastic parameters through comparative analysis of different blocky inversion prior models.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Time-lapse Difference Inversion Based on the exact Zoeppritz Equations with Blockiness Constraint

Zhou

Liao

et al. 2020

JEEG

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Accurately inverting changes in the reservoir elastic parameters that are caused by oil and gas exploitation is of great importance in accurately describing reservoir dynamics and enhancing recovery. Previously numerous time-lapse seismic inversion methods based on the approximate formulas of exact Zoeppritz equations or wave equations have been used to estimate these changes. However the low accuracy of calculations using approximate formulas and the significant calculation effort for the wave equations seriously limits the field application of these methods. However, these limitations can be overcome by using exact Zoeppritz equations. Therefore, we study the time-lapse seismic difference inversion method using the exact Zoeppritz equations. Firstly, the forward equation of time-lapse seismic difference data is derived based on the exact Zoeppritz equations. Secondly, the objective function based on Bayesian inversion theory is constructed using this equation, with the changes in elastic parameters assumed to obey a Gaussian distribution. In order to capture the sharp time-lapse changes of elastic parameters and further enhance the resolution of the inversion results, the blockiness constraint, which follows the differentiable Laplace distribution, is added to the prior Gaussian background model. All examples of its application show that the proposed method can obtain stable and reasonable P- and S-wave velocities and density changes from the difference data. The accuracy of estimation is higher than for existing methods, which verifies the effectiveness and feasibility of the new method. It can provide high-quality seismic inversion results for dynamic detailed reservoir description and well location during development.

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Section: Introductionmentioning

confidence: 99%

Bayesian Time-lapse Difference Inversion Based on the exact Zoeppritz Equations with Blockiness Constraint

Zhou

Liao

et al. 2020

JEEG

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Prediction of residual oil saturation by using the ratio of amplitude attributes of time-lapse seismic data

Liu

et al. 2017

GEOPHYSICS

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Subtraction of baseline and monitoring seismic data is a common step in highlighting reservoir changes in time-lapse seismic interpretation. However, ambiguity exists in the interpretation of the amplitude difference, which is controlled by fluid change and reservoir thickness. To estimate the residual oil saturation quantitatively, we have developed a time-lapse seismic interpretation method that uses the ratio of amplitude attributes extracted from the baseline and monitoring seismic data. The relationship between impedance change and the ratio of the baseline and monitoring amplitude attributes is determined to avoid the influence of reservoir thickness. Subsequently, the fluid saturation is calculated from the impedance change by using a proper petrophysical relationship. We have tested our new method on a real time-lapse seismic data set from a water-flooded reservoir in the deepwater area of West Africa. The water-flooded area determined from the amplitude difference does not completely match the production logs because of the influence of variations in the reservoir thickness. However, the residual oil distribution calculated with the proposed method matches the production logs well. The connectivity of sandstone bodies is also evaluated based on an integrated interpretation of estimated oil saturation. With its simple principles and easy accessibility, our method improves the accuracy of time-lapse seismic data interpretation in water-flooded oil reservoirs. Furthermore, the quantitative interpretation of fluid change enables the time-lapse seismic technology to guide reservoir development directly.

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Time-lapse difference inversion based on the modified reflectivity method with differentiable hyper-Laplacian blocky constraint

Tang

Zhang

et al. 2021

GEOPHYSICS

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Time-lapse (TL) seismic has great potential in monitoring and interpreting time-varying variations in reservoir fluid properties during hydrocarbon exploitation. Obtaining the disparities of reservoir elastic parameters by inversion is essential for TL reservoir monitoring. Conventional TL inversion is carried out by stages without coupling processing and leads to inaccurate results. We directly use the differences in seismic data responses from different vintages, namely, difference inversion, to improve the results’ credibility. It may reduce the deviations of the subtraction of base and monitor inversions in traditional methods. Moreover, most existing studies involving prestack inversion methods use the Zoeppritz equation or its approximants, which failed to consider the wave-propagation effects. We have developed a new TL difference inversion based on the modified reflectivity method (MRM), in which the internal multiples and transmission losses are taken into consideration to fine-tune the characterization of the seismic wave propagating underground. The new method is modified on the basis of the reflectivity method making it feasible in TL difference inversion, and it is derived from the Bayesian theorem. For further delineating the boundaries between layers, the differentiable hyper-Laplacian blocky constraint (DHLBC) is introduced into the prior information of a Bayesian framework, which heightens the sparseness in the vertical gradients of the inversion results and leads to sharp interlayer boundaries of the difference parameters. Synthetic and field data experiments demonstrate that our TL difference inversion method has clear advantages in accuracy and resolution compared with the Zoeppritz method and MRM without DHLBC.

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Blocky inversion of time‐lapse seismic AVO data

Cited by 3 publications

References 3 publications

Bayesian Time-lapse Difference Inversion Based on the exact Zoeppritz Equations with Blockiness Constraint

Bayesian Time-lapse Difference Inversion Based on the exact Zoeppritz Equations with Blockiness Constraint

Prediction of residual oil saturation by using the ratio of amplitude attributes of time-lapse seismic data

Time-lapse difference inversion based on the modified reflectivity method with differentiable hyper-Laplacian blocky constraint

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