Simultaneous inversion of petrophysical parameters based on geostatistical a priori information

Yin, Xingyao; Sun, Rongchuan; Wang, Baoli; Zhang, Guangzhi

doi:10.1007/s11770-014-0445-1

Cited by 25 publications

(5 citation statements)

References 19 publications

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“…The R-values show strong positive correlations at the near and mid angles, while the correlation is moderate at the far angle. This is matching with Thomsen's equations in ( 9) and (10), which consider a linear relationship between the zero-offset velocity and the velocity at an angle theta.…”

Section: Partial-log-constrained Inversionsupporting

confidence: 77%

“…The separate inversion of angle stacks is based on a partial inversion approach that obtains the zero-offset P-and S-wave velocities from the inverted P-and S-waves at different angular ranges [90]. The idea is based on Thomsen's equations in ( 9) and (10), which relate the vertical P-wave (V P ) and S-wave (V S ) velocities to the anisotropy parameters, Epsilon and Delta, in VTI media.…”

Section: Partial-log-constrained Inversionmentioning

confidence: 99%

“…Similarly, the linear expression of the Z S0 model was obtained based on Thomsen's equation in (10), which can be rewritten as shown below:…”

Section: Zero-offset Impedance Modeling 431 Statistical Modelingmentioning

confidence: 99%

“…Other methodologies consider reservoir parameter inversion based on Bayesian classification theory [8,9]. Moreover, a geostatistical inversion method was used to predict reservoir properties in a higher certainty than that of deterministic methods [10]. In addition, some studies derived elastic properties from prestack seismic inversion to define facies [11,12].…”

Section: Introductionmentioning

confidence: 99%

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Estimation of Litho-Fluid Facies Distribution from Zero-Offset Acoustic and Shear Impedances

2022

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Seismic data are considered crucial sources of data that help identify the litho-fluid facies distributions in reservoir rocks. However, different facies mostly have similar responses to seismic attributes. In addition, seismic anisotropy negatively affects the facies predictors extracted from seismic data. Accordingly, this study aims at estimating zero-offset acoustic and shear impedances based on partial-stack inversion by two methods: statistical modeling and a multilayer feed-forward neural network (MLFN). The resulting impedance volumes are compared to those obtained from isotropic simultaneous inversion by using impedance logs. The best impedance volumes are applied to Thomsen’s anisotropy equations to solve for the anisotropy parameters Epsilon and Delta. Finally, the shear and acoustic impedances are transformed into elastic properties from which the facies and fluid distributions are predicted by using the logistic regression and decision tree algorithms. The results obtained from the MLFN show better matching with the impedance and facies logs compared to those obtained from isotropic inversion and statistical modeling.

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Section: Partial-log-constrained Inversionsupporting

confidence: 77%

Section: Partial-log-constrained Inversionmentioning

confidence: 99%

“…Similarly, the linear expression of the Z S0 model was obtained based on Thomsen's equation in (10), which can be rewritten as shown below:…”

Section: Zero-offset Impedance Modeling 431 Statistical Modelingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Estimation of Litho-Fluid Facies Distribution from Zero-Offset Acoustic and Shear Impedances

2022

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“…As different models can be generated by varying Gaussian white noise, FFT‐MA method can well evaluate the uncertainty of reservoir. In recent years, FFT‐MA has been widely used in reservoir stochastic modelling, which has made outstanding progress in the study of high‐resolution reservoir elastic property modelling and high‐resolution reservoir heterogeneity characterization (Yang et al., 2018; Yang et al., 2022; Yin et al., 2014). As for the autocorrelation function of FFT‐MA method, the commonly used types include von Karman, Gaussian, exponential and hybrid (Goff & Jennings, 1999; Ikelle et al., 1993; Xi & Yao, 2004).…”

Section: Introductionmentioning

confidence: 99%

High‐resolution reservoir stochastic modelling based on optimized estimation of vertical autocorrelation

Zeng

Zhang

et al. 2023

Geophysical Prospecting

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The high‐resolution model of elastic properties is of great significance for fine reservoir characterization and precise oil and gas exploration. However, it is difficult to obtain a satisfactory high‐resolution reservoir model with the existing technologies. In this paper, a novel high‐resolution stochastic modelling strategy based on the fast Fourier transform moving average is proposed. In this strategy, several structural parameters are optimized to improve the rationality of the stochastic model, including vertical autocorrelation length, horizontal autocorrelation length, roughness factor and angle parameter. Among them, the optimization of the vertical autocorrelation length is crucial for vertical high‐resolution modelling. To this end, a nonlinear optimal inversion strategy of the vertical autocorrelation length is designed based on the idea of minimizing the spectral Jensen–Shannon divergence between the modelling result and the logging curve. However, nonlinear inversion is usually unstable, so it is necessary to introduce a regularization operator in the inversion to improve the stability. Considering that the heterogeneity of the subsurface medium is consistent or gradual within the stratums, but discontinuous and abrupt at the interfaces, edge‐preserving regularization is applied to obtain a blocky estimation of the vertical autocorrelation length. The optimal estimation experiment of the vertical autocorrelation length based on the measured logging data shows that the edge‐preserving regularization significantly improves the stability of the nonlinear optimal inversion, and blocky estimation results with sharp edges are obtained. Then, the optimized vertical autocorrelation length and the other structural parameters are applied to fast Fourier transform moving average modelling on an actual reservoir profile. The result shows that the resolution of the model is significantly improved, which realizes the fine reservoir characterization. In addition, the optimized structural parameters effectively constrain the small‐scale heterogeneity and ensure the rationality of the stochastic model.

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Reservoir parameter inversion based on weighted statistics

et al. 2015

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Simultaneous inversion of petrophysical parameters based on geostatistical a priori information

Cited by 25 publications

References 19 publications

Estimation of Litho-Fluid Facies Distribution from Zero-Offset Acoustic and Shear Impedances

Estimation of Litho-Fluid Facies Distribution from Zero-Offset Acoustic and Shear Impedances

High‐resolution reservoir stochastic modelling based on optimized estimation of vertical autocorrelation

Reservoir parameter inversion based on weighted statistics

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