Joint probabilistic fluid discrimination of tight sandstone reservoirs based on Bayes discriminant and deterministic rock physics modeling

Wang, Pu; Li, Jingye; Chen, Xiaohong; Wang, Benfeng

doi:10.1016/j.petrol.2020.107218

Cited by 28 publications

(2 citation statements)

References 40 publications

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“…For anisotropy caused by fractures, and there are a lot of scholars and experts doing research on this, Anisotropic petrophysical models based on the equivalent medium theory include anisotropic differential equivalent medium model [3]- [6] , Anisotropic Self-consistent Model (SCA) [7]- [8] , Anisotropic Backus model et al [9] . Rock physical models considering different pore structure and fracture development characteristics include Hudson model [10]- [11] , Linear sliding model [12]- [18] and anisotropic soft pore model et al [19]- [22] . On this basis, Cheng established the crack equivalent medium model applicable to arbitrary aspect ratio Eshelby-Cheng model [23]- [25] , It breaks through the limitation that Hudson model can only be applied to relatively large fracture aspect ratio.…”

Section: Introductionmentioning

confidence: 99%

Wave velocity prediction based on anisotropic SCA and Eshelby-Cheng model

Gao,

Wang,

Jin

2024

Fourth International Conference on Geology, Mapping, and Remote Sensing (ICGMRS 2023)

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Fractured reservoirs have become the main research object of oil and gas exploration and development. It has become a practical research object to establish a rock physical model for anisotropy caused by fractures. Combined with anisotropic SCA model and Eshelby-Cheng model, a rock physical model is established to predict the velocity of the longitudinal and transverse waves. The physical property parameters of minerals are obtained by logging data, and the elastic modulus of rock matrix is calculated based on V-R-H model. Then the elastic modulus of dry rock skeleton is calculated by the anisotropic SCA model. Finally, Eshelby-Cheng model is used to obtain the elastic modulus of saturated rock and predict the velocity of rock wave. The comparison and analysis of the obtained P and S wave velocities with the actual data shows that The longitudinal and shear wave velocities of saturated rock calculated by the rock physical model are in good agreement with the actual data, which conforms to the actual geological conditions, and proves that the method is reliable and effective.

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Section: Introductionmentioning

confidence: 99%

Wave velocity prediction based on anisotropic SCA and Eshelby-Cheng model

Gao,

Wang,

Jin

2024

Fourth International Conference on Geology, Mapping, and Remote Sensing (ICGMRS 2023)

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“…HE accuracy of reservoir characterization usually relies on the analysis of subsurface attributes (e.g., velocity, density and impedance). Amplitude-variation-with-offset/angle (AVO/AVA) inversion is a frequent methodology to obtain velocity and density from recorded seismogram [1][2][3][4]. Due to the advantage of low cost, the convolutional model is still the most widely used method in the oil or gas industry, i.e., the seismic record is represented by convolving the reflection coefficient with seismic wavelet [5].…”

Section: Introductionmentioning

confidence: 99%

Hybrid-Driven High-Resolution Prestack Seismic Inversion

Zhang,

Zhao,

Sun

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Pre-stack seismic inversion is considered among the most frequently utilized techniques for reservoir characterization. However, the resolution of the inverted parameters, such as P-, S-wave velocity and density, is low due to the limited band-width and side-lobe interference of the seismic wavelet. To address this issue, a hybrid two-step strategy that combines data-driven and model-driven methods is proposed to enable higher resolution and accuracy of the inverted results. We first construct a three-layer fully connected network to implement the mapping of seismic data to reflection coefficients. The method does not require exact seismic wavelet to be known and intensive human-computer interaction. It estimates reflectivity based on the extracted features from training data, which gives more accurate results compared to traditional sparse inversion methods. Then, the model-driven method (i.e., amplitude variation with offset/angle inversion method) is adopted to reconstruct P-wave velocity, S-wave velocity and density from the estimated reflection coefficients. The performance of the hybrid-driven strategy (HDS) is checked using synthetic model and real data. The results indicate that the proposed method provides more accurate and higher resolution inversion results for seismic reservoir characterization.

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