A new model of pore structure typing based on fractal geometry

Chen, Xiaojun; Yao, Guangqing; Herrero‐Bervera, E.; Cai, Jianchao; Zhou, Kun; Luo, Chengfei; Jiang, Ping; Jiang, Lu

doi:10.1016/j.marpetgeo.2018.08.023

Cited by 33 publications

(15 citation statements)

References 53 publications

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“…[55,56]. Therefore, due to the complexity of pore structures, the data points do not fall on a regression curve in the research area, while they are usually scattered in a banded range [55]. As mentioned above, our work showed that the fractal geometry based on different tests has a significant effect on the pore structure typing; hence, a combination method was chosen to determine the reasonable pore structure classification.…”

Section: Pore Size Fractal Dimensionmentioning

confidence: 77%

“…It will be analyzed subsequently. [55,56]. Therefore, due to the complexity of pore structures, the data points do not fall on a regression curve in the research area, while they are usually scattered in a banded range [55].…”

Section: Pore Size Fractal Dimensionmentioning

confidence: 99%

“…detrital grains and resisted the mechanical compaction, or they would clog the pore-throat system directly, while illite fulfilled the void spaces and deteriorated the storage and percolation directly; therefore, type N1 sandstones generally have greater reservoir quality than type N2 (Table 1). Besides, the products of the dissolution are precipitated into interstitial minerals, such as carbonate (ferrocalcite and ankerite), quartz cement, and clay, which would also block the pores and narrow the throats, and have a very limited contribution to the improvement of the petrophysical properties, especially the intraparticle dissolution pores [55]. The intergranular pores generated by relatively large grains could enhance the storage and percolation capacity.…”

Section: Geofluidsmentioning

confidence: 99%

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Quantifying Fractal Dimension in Integrated Experimental Data of Tight Sandstones

Ren

Liu

et al. 2019

Geofluids

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Determining the microscopic pore structures of tight sandstones is becoming one of the most challenging efforts, and the strong heterogeneity makes the accurate assessment still a problem. In this research, we report a new criterion for pore structure typing based on the fractal geometry theory. The fractal dimension values were first accurately calculated through intrusion and nonintrusion methods. The results show that the pores in tight sandstones have multifractal distributions and different types of pore structure were divided based on various tests. The relationships between petrophysical properties and a series of multifractal parameters have been analyzed in detail. The fractal dimension values of type 1 sandstones derived from the throat curves in rate-controlled mercury intrusion methods can well characterize the porosity of the research area, while the others did not respond well. Finally, a multifractal criterion was proposed to analyze the petrological and pore structures by combined observations and experiments. The new criterion exhibits perfect performance in the prediction of the storage capacity. The multifractal model proposed in this research helps to assess the pore structures of tight sandstones and helps to characterize the reservoir quality in hydrocarbon exploration and development.

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Section: Pore Size Fractal Dimensionmentioning

confidence: 77%

Section: Pore Size Fractal Dimensionmentioning

confidence: 99%

Section: Geofluidsmentioning

confidence: 99%

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Quantifying Fractal Dimension in Integrated Experimental Data of Tight Sandstones

Ren

Liu

et al. 2019

Geofluids

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“…Equation shows that the inherent difference in the pore structure between 2‐D and 3‐D is tortuosity. Generally, the more complex the pore structure is, the more pores there are at the nanoscale, and their tortuosity values (also known as the structural coefficient) are higher, even at the same pore radius (Chen et al, ). A pore channel with a poor connectivity also gives a relatively large tortuosity.…”

Section: Methodology and Model Developmentmentioning

confidence: 99%

Capillary Pressure Curve Determination Based on a 2‐D Cross‐Section Analysis Via Fractal Geometry: A Bridge Between 2‐D and 3‐D Pore Structure of Porous Media

Chen

Yao

Luo³

et al. 2019

JGR Solid Earth

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Pore structure is a crucial attribute in characterizing fluid flow through porous media. However, direct experimental measurements or numerical reconstructions are commonly expensive and not environmentally friendly, with great uncertainty caused by the complex nature of porous media. In this study, we demonstrate that one special bridge function, which is a function of the apparent length and tortuosity fractal dimension, can characterize the relationship of pore structures between two dimensions (2‐D) and three dimensions (3‐D), and it can serve as a conversion bridge of the radius to determine the capillary pressure curve (CPC). We compare estimations by the proposed method with experimental results obtained by mercury intrusion porosimetry in six typical natural sandstones with varying porosities and permeabilities. The result shows that cross sections of the global pore structure, such as thin section, electronic probe, and microcomputed tomography slices, give a reliable estimation of the CPC using the bridge function in porous media with a medium porosity. However, in unconventional porous media with a relatively low porosity (~10%) or extra high porosity (~30%), due to the empirical nature of the equation widely used to calculate the tortuosity fractal dimension, the necessary modification is necessary to obtain the CPC when applying the bridge function in such porous media. This insight can significantly simplify the procedure for obtaining the petrophysical properties of a porous medium, which may shed light on the inherent differences and correlations between the 2‐D and 3‐D pore structures of porous media.

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“…Fractal-based analytic expressions (Equations (22) and (23)) of the relative permeability contain three parameters, i.e., c, ϕ, and D f . ϕ is the porosity of porous media determined by experiments, c is the nonuniformity factor of pore cross section, and D f is the fractal dimension determined by the best-fitting or the box-counting method [47,48]. In this section, model parameters are also determined by fitting measured data and calculating the minimum RMSE between prediction values and experimental data.…”

Section: Relative Permeability-saturation Curvementioning

confidence: 99%

Relative Permeability of Porous Media with Nonuniform Pores

et al. 2020

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Most classical predictive models of relative permeability conceptualize the pores in porous media as assemblies of uniform capillary tubes with different sizes. However, this simplification may overestimate the transport capacity of porous media due to overlooking the effects of the pore nonuniformity. This study presents a simple way to quantify the effect of the nonuniformity of pore cross section on the transport characteristic of unsaturated porous media. The way is based on the index relationship between the porosity of a newly defined reference cross section and that of porous media, which satisfies the intrinsic constraints for the nonuniform porosity of cross sections in porous media. Moreover, the index factor can be captured by a newly defined parameter, called the nonuniformity factor, which is used to establish an extended Darcy’s law. Based on these, a fractal-based continuous analytical model and a fractal-based Monte Carlo model of relative permeability as well as a permeability-porosity model are established. Experimental data of five wetting-nonwetting phase systems, including the water-air, water-steam, water-nitrogen, water-oil, and oil-gas systems, are selected to assess the performance of the proposed model. The results confirm the proposed model’s capacity in capturing the transport properties of various porous media. It is found that the nonuniformity of pores can significantly increase the resistance of fluid flow and thus reduce the transport capacity of porous media.

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A new model of pore structure typing based on fractal geometry

Cited by 33 publications

References 53 publications

Quantifying Fractal Dimension in Integrated Experimental Data of Tight Sandstones

Quantifying Fractal Dimension in Integrated Experimental Data of Tight Sandstones

Capillary Pressure Curve Determination Based on a 2‐D Cross‐Section Analysis Via Fractal Geometry: A Bridge Between 2‐D and 3‐D Pore Structure of Porous Media

Relative Permeability of Porous Media with Nonuniform Pores

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