Rock physics modelling of porous rocks with multiple pore types: a multiple‐porosity variable critical porosity model

Zhang, Jiajia; Yingyao, Yin; Zhang, Guangzhi

doi:10.1111/1365-2478.12898

Cited by 16 publications

(4 citation statements)

References 39 publications

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“…The more obvious the amplitude difference, the better the oil content. [47][48][49] As shown in Well F854, the resistivity in the 1808~1810m section is 13.4Ω•m, and the oil test conclusion is that the oil layer has a daily oil production of 3.74t and water production of 0.66m 3 .…”

Section: A Spontaneous Potential (Sp)overlapmentioning

confidence: 95%

Genetic mechanisms and multi-parameter logging identification of low-resistivity oil pay: A case study of the Triassic Chang 6 member, Zhidan area, Ordos Basin, China

Wang

Zhu

Zhang³

2023

Preprint

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Low-resistivity pay have been found throughout the world, the identification and characterization of low-resistivity pay is very challenging and important for the development of oil fields. The genesis of the low-resistivity oil pay is complex, and the logging response characteristics are variable. The weak difference in resistivity between the oil pay and the adjacent water pay makes it difficult to identify kinds of fluids by resistivity log analysis, which reduces the overall exploration benefit of the oilfield. Therefore, it is very important to study the genesis and identification technology of the low-resistivity oil pay. In this paper, we first analyzed the core experimental results such as X-ray diffraction scanning electron microscope, mercury intrusion, phase permeability, nuclear magnetic resonance, physical properties, electric petrophysical experiment, micro-CT technology and rock wettability, etc. Comprehensive analysis of the reservoir characteristics shows that the development of low-resistivity oil pays in the study area is controlled by irreducible water saturation and high gamma ray sandstone. The complicated pore structure and rock hydrophilicity are the factors that lead to the increase of irreducible water saturation. Then, the salinity of formation water and the invasion of drilling fluid also have a certain influence on the change of reservoir resistivity. According to the controlling factors of the low- resistivity oil pay, we extract the sensitive parameters to the logging response, amplify the difference between oil and water pay, and use the AC-RILD, SP-PSP, GR*GR*∆SP-RILD and(RILM-RILD)/RILD—RILD cross-plots, etc. Various methods such as cross-plots method, overlap method and movable water analysis are mutually constrained to identify low-resistivity oil pays. In the case study, the comprehensive application of the above identification flow path can effectively improve the accuracy of fluid recognition step by step. It provides reference for identifying more low-resistivity reservoirs with similar geological conditions.

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Section: A Spontaneous Potential (Sp)overlapmentioning

confidence: 95%

Genetic mechanisms and multi-parameter logging identification of low-resistivity oil pay: A case study of the Triassic Chang 6 member, Zhidan area, Ordos Basin, China

Wang

Zhu

Zhang³

2023

Preprint

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“…The corresponding scattering velocity-porosity relationships were then reasonably interpreted by varied crack concentrations. Further, the sophisticated double-porosity model [10,11] and the multiple-porosity model [12] were proposed to explain tight sandstone's poroelastic behaviors accurately. Experimental investigations confirmed the applicability of the double-porosity model for tight sandstones [13,14].…”

Section: Introductionmentioning

confidence: 99%

Pore and Microfracture Characterization in Tight Gas Sandstone Reservoirs with a New Rock-Physics-Based Seismic Attribute

Guo¹,

Qin²,

Liu³

2023

Remote Sensing

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Pores and microfractures provide storage spaces and migration pathways for gas accumulation in tight sandstones with low porosity and permeability, acting as one of the controlling factors of gas production. The development of a rational rock physics model is essential for better understanding the elastic responses of tight sandstone with complex pore structures. Accordingly, seismic characterization of pores and microfractures based on the rock physics model provides valuable information in predicting high-quality tight gas sandstone reservoirs. This paper proposes a rock-physics-based approach to compute the pore–microfracture indicator (PMI) from elastic properties for pore structure evaluation in tight sandstones. The PMI is achieved based on the axis rotation of the elastic parameter space using well-log data. The rotation angle is determined by finding the maximum correlation between the linearized combination of the elastic parameters and the introduced factor associated with total porosity and microfracture porosity. The microfracture porosity is then estimated with an inversion scheme based on the double-porosity model. Finally, the optimized rotation angle is employed to compute the PMI with seismic data. The obtained results are of great benefit in predicting the permeable zones, providing valuable information for sweet spot characterization in tight gas sandstone reservoirs.

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“…It is proven that this method can effectively solve the problem of S-wave velocity prediction in the area lacking S-wave logging data (Zhu et al, 2017). Zhang et al (2019) and Zhang et al (2020a) pointed out that the reservoir can be better described based on the rock physical modeling of two-phase media, and the concept of critical porosity can also be used to establish the empirical relationship between the grain matrix and dry rock, providing parameters for fluid identification.…”

Section: Introductionmentioning

confidence: 99%

Rock Physical Modeling of Tight Sandstones Based on Digital Rocks and Reservoir Porosity Prediction From Seismic Data

et al. 2022

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Digital rock physics (DRP) has become an important tool to analyze the characteristics of pore structures and minerals and reveal the relationships between microscopic structures and the physical properties of reservoirs. However, it is greatly difficult to upscale the rock physical parameters, such as P-wave velocity, S-wave velocity, and elastic moduli, from DRP to large-scale boreholes and reservoirs. On the other hand, theoretical rock physical modeling can establish the internal relationship between the elastic properties and physical parameters of tight sandstones, which provides a theoretical basis for seismic inversion and seismic forward modeling. Therefore, the combination of digital rock physics and rock physical modeling can guide the identification and evaluation of the gas reservoir’s “sweet spot.” In this study, the CT images are used to analyze the mineral and pore characteristics. After that, the V-R-H model is used to calculate the equivalent elastic moduli of rocks containing only the mineral matrix, and then, the differential equivalent medium (DEM) model is used to obtain the elastic moduli of dry rocks containing minerals and pores. Subsequently, the homogeneous saturation model is used to fill the fluids in the pores and the Gassmann equation is used to calculate the equivalent elastic moduli of the saturated rock of tight sandstones. Rock physical modeling is calibrated, and the reliability of the rock physical model is verified by comparing those with the logging data. Afterward, the empirical relationship of rock porosity established from CT images and rock elastic moduli is obtained, and then, the elastic parameters obtained by seismic data inversion are converted into porosity parameters by using this empirical relationship. Finally, the porosity prediction of large-scale reservoirs in the study area is realized to figure out the distribution of gas reservoirs with high porosity. The results show that the H3b and H3c sections of the study area exhibit higher porosity than H3a. For the H3b reservoir, the northeast and middle areas of the gas field are potential targets since their porosity is larger than that of others, from 10% to 20%. Because of the effects of the provenance from the east direction, the southeast region of the H3c reservoir exhibits higher porosity than others.

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Rock physics modelling of porous rocks with multiple pore types: a multiple‐porosity variable critical porosity model

Cited by 16 publications

References 39 publications

Genetic mechanisms and multi-parameter logging identification of low-resistivity oil pay: A case study of the Triassic Chang 6 member, Zhidan area, Ordos Basin, China

Genetic mechanisms and multi-parameter logging identification of low-resistivity oil pay: A case study of the Triassic Chang 6 member, Zhidan area, Ordos Basin, China

Pore and Microfracture Characterization in Tight Gas Sandstone Reservoirs with a New Rock-Physics-Based Seismic Attribute

Rock Physical Modeling of Tight Sandstones Based on Digital Rocks and Reservoir Porosity Prediction From Seismic Data

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