A Direct Estimation Method for the Russell Fluid Factor Based on Stochastic Seismic Inversion

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

doi:10.1002/cjg2.20230

Cited by 4 publications

(2 citation statements)

References 18 publications

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“…The sampling optimization in the FCMCMC algorithm-based geostatistical inversion is achieved by leveraging the weighted, lateral changes in the seismic waveforms. In this process, the concept of an effective sample size is introduced to reflect the degree to which the spatial changes in the seismic waveforms affect the delineation of the reservoir's geometry [39][40][41]. The effective sample size is defined as the number of effective samples that can be used to estimate the inversion results at a prediction point, and it can be determined as follows.…”

Section: Determination Of the Effective Sample Sizementioning

confidence: 99%

Theory and Application of Geostatistical Inversion: A Facies-Constrained MCMC Algorithm

Dong

Gui

et al. 2023

Processes

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To improve the prediction of thin reservoirs with special geophysical responses, a geostatistical inversion technique is proposed based on an in-depth analysis of the theory of geostatistical inversion. This technique is based on the Markov chain Monte Carlo algorithm, to which we added the contents of facies-constrained. The feasibility of the technique and the reliability of the prediction results are demonstrated by a prediction of the sand bodies in the braided river channel bars in the Xiazijie Oilfield in the Junggar Basin. Based on the MCMC algorithm, the results show that leveraging the lateral changes in the seismic waveforms as geologically relevant information to drive the construction of the variogram and the optimization of the statistical sampling can largely overcome the obstacle that prevents traditional geostatistical inversions from accurately delineating the sedimentary characteristics; thereby, the proposed algorithm truly achieves facies-constrained geostatistical inversion. The case study of the Xiazijie Oilfield showed the feasibility and reliability of this technology. The prediction accuracy of the FCMCMC algorithm-based geostatistical inversion is as high as 6 m for thin interbedded reservoirs, and the coincidence rate between the prediction results and the well log data is more than 85%, which confirms the reliability of the technique. The demonstrated performance of the proposed technique provides a preliminary reference for the prediction of the thin interbedded reservoirs formed in terrestrial sedimentary basins and characterized by small thicknesses and rapid lateral changes with special geophysical responses.

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Section: Determination Of the Effective Sample Sizementioning

confidence: 99%

Theory and Application of Geostatistical Inversion: A Facies-Constrained MCMC Algorithm

Dong

Gui

et al. 2023

Processes

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“…Sun, et al (2015) proposed a novel fracture fluid factor that can simultaneously detect fracture development and fluid properties by combining P-wave anisotropic fracture prediction and Russell fluid factor into the Cartesian coordinate system in order to address the challenge of fluid identification in anisotropy, which achieved promising application in igneous areas. Sun et al (2016) employed sequential Gaussian simulation and Metropolis sampling algorithm based on Bayesian's theoretical framework to directly estimate the Russell fluid factor, which enhanced the accuracy of fluid factor identification. Although substantial developments have been made in recent years regarding the algorithms of fluid prediction, the issue of fluid prediction under non-homogeneous conditions still faces challenges such as strong multi-solution and less precise prediction.…”

Section: Introductionmentioning

confidence: 99%

Bayesian fluid prediction by decoupling both pore structure parameter and porosity

He,

Wang,

Wang

et al. 2023

Front. Earth Sci.

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Carbonate reservoirs exhibit complex pore structure, which significantly affects the elastic properties and seismic response, as well as the prediction of physical parameters. As one of the main factors impacting fluid prediction, pore structure parameter directly involves in few inversion methods. In order to directly predict pore structure parameter in inversion, a novel quantitative reflection coefficient formula is proposed, that integrate Russell's poroelasticity theory with Sun's petrophysical model. This formula separates fluid bulk modulus from porosity and pore structure parameter, allowing for accurate determination of pore-fluid distribution through Bayesian framework. Both theoretical model analysis and multi-component digital core experiments of carbonates validate the importance of pore structure parameter on fluid identification. The practical application of carbonate reservoirs in Sichuan Basin demonstrates that the proposed fluid factor, eliminating the prediction illusion caused by heterogeneity in porosity and pore structure parameter within strata, provides more precise and reliable predictions compared to the Russell fluid factor. Furthermore, the similarity between the Russell fluid factor obtained directly from the Russell approximation and the Russell fluid factor calculated indirectly from the proposed method confirms the stability and accuracy of the new reflection coefficient formula.

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Fluid identification in fractured media with genetic algorithm

Li,

Yang,

Wang

2024

Journal of Applied Geophysics

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A Direct Estimation Method for the Russell Fluid Factor Based on Stochastic Seismic Inversion

Cited by 4 publications

References 18 publications

Theory and Application of Geostatistical Inversion: A Facies-Constrained MCMC Algorithm

Theory and Application of Geostatistical Inversion: A Facies-Constrained MCMC Algorithm

Bayesian fluid prediction by decoupling both pore structure parameter and porosity

Fluid identification in fractured media with genetic algorithm

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