Quantitative characterization of pores and throats of tight sandstone reservoirs based on CT scan: A case study of the Penglaizhen Formation in the Sichuan Basin,China

Abstract

The accurate extraction of pore-throat information, particularly in pore-throat size, pore connectivity, and pore morphology, is of great significance for the physical transport of porous media. However, traditional methods for pore-throat information extraction often rely on petrophysical methods, exhibiting evident weakness in achieving visual and quantitative characterization. In this article, an integrated analysis of petrography, micro-computed tomography (CT), and conventional petrophysical methods (X-ray diffraction, casting thin section, and scanning electron microscopy) was combined to realize visual and quantitative characterization of pore structure in tight sandstone reservoirs. Based on CT and image processing technology, a three-dimensional (3D) digital core model was reconstructed. Characteristic parameters describing pore morphology, shape factor, sphericity, and equivalent diameter were defined, and the quantitative classification of 3D pore morphology was carried out. The results reveal that the predominant pore types in the study area are primary intergranular pores, intergranular dissolved pores, intra-granular dissolved pores, and inter-crystalline pores, and typical types of throats are necked, lamellar, and lamellar curved throats. Moreover, the pore structure of tight sandstone consists of both isolated pores and interconnected pores, and the volume of interconnected pores accounts for 83.47% of the total pore volume, while the volume of isolated pores accounts for an average of 16.53% of the total pore volume. In addition, the pore–throat ratio is mainly distributed between 2 and 7, and the coordination number is mainly distributed between 1 and 5. Besides, the pore morphologies of tight sandstone are classified into spherical pores, triangular pores, banded pores, and harbor-like pores. Specifically, spherical pores and triangular pores dominate in pore number, but minimally in pore volume and surface area. In contrast, banded pores and harbor-like pores together constitute the majority of the pore volume (>80.64%) of the Penglaizhen Formation in the Sichuan Basin. This study enables accurate characterization of microscopic pore-throat features in tight sandstone reservoirs and provides a strong theoretical support for the subsequent research on physical transport in tight sandstone reservoirs.

Keywords

Tight sandstone reservoirs digital core reconstruction pore morphology shape factor banded pores and harbor-like pores

Introduction

The efficient development of unconventional resources is a crucial measure to ensure energy supplement and promote economic growth (Li et al., 2025; Zhao et al., 2025). Tight sandstone gas, as an important component of natural gas, has great potential for development (Li et al., 2022, 2023). However, due to significant differences in size, shape, volume, and morphological structure of tight sandstones, the fine characterization of the micropore structure in tight sandstone reservoirs by conventional petrophysical methods has become increasingly difficult.

Recently, the exploration of pore structures in tight sandstone reservoirs through micron-computer tomography (CT) three-dimensional (3D) reconstruction represents a seminal advancement in the field of geological sciences (Li et al., 2024). CT technology is not only effectively capable of reconstructing 3D digital core modeling, but also extracting the characteristic parameters of pore structure (Karimpouli et al., 2020; Liu et al., 2017; Sun et al., 2023; Zhao et al., 2020), and has attracted tremendous attention from researchers. Bai et al. (2013), Xiao et al. (2016), and Wang et al. (2020) studied the pore morphology of tight sandstone by multiscale CT scanning. The results revealed that the micropore structure of tight sandstone shows strong heterogeneity, and large pores are distributed in irregular bundles and banded pores with good connectivity. While small pores are mostly ellipsoid, spherical, and point-like, and are distributed in isolation. Additionally, Li et al. (2016, 2017) studied the pore morphology of the tight sandstone in Fuyu Reservoir, Da’an Oilfield, southern Songliao Basin. The results showed that the pore morphology is mainly coarse tubular and banded at the micron scale, accompanied by strong heterogeneity. While the pore morphology is mainly spherical and tube bundle at the nanoscale pores with poor connectivity. Yang et al. (2016, 2017) proposed the length–width ratio $(L / d)$ , and the pore morphology of the tight sandstone were divided into three types: tube bundle ( $L / d \geq 3$ ), irregular ( $1.3 \leq L / d \leq 3$ ), and spherical ( $1 \leq L / d \leq 1.3$ ). Li (2020) reconstructed the digital core model of soil and referred to the shape factor. As a result, the pores in soil are divided into spheroid pores, columnar pores, branched pores, and cracks in turn. Concurrently, Qin et al. (2021) analyzed the correlation between the pore morphology and oiliness using a semi-quantitative method, and the results demonstrated that sandstones with smooth pore edges and a well-aligned pore orientation generally have high oiliness, whereas sandstones with lower oiliness have ragged pore edges and a chaotic pore orientation. Meng et al. (2023) and Guo et al. (2023) have carried out the classification on both pore morphology and microfracture based on the μ-CT scanning technology. More importantly, Liu and Zhang (2025) utilized high-resolution nano-CT technology to carry out the relationship between the pore morphology and physical properties (porosity and permeability), and the results indicated that the number of pores in tight sandstones shows a minor impact on their porosity. While large pores (more than 20 mm) contribute predominantly to porosity, and throats with average radii > 2 mm can significantly improve the permeability of tight sandstones.

The previous studies may focus on qualitative and semi-quantitative characterization of pore morphology of tight sandstone, and little work has been devoted to visual and quantitative characterization and classification of 3D pore morphology in tight sandstone reservoirs. This article studies the tight sandstone of the Penglaizhen Formation in the Sichuan Basin. It aims to realize visual and quantitative characterization and classification of pore morphology in tight sandstone using digital core reconstruction technology (micro-CT) and traditional petrophysical methods, such as scanning electron microscopy (SEM), casting thin section (CTS), and X-ray diffraction (XRD). On this basis, 3D digital core models for characterizing the pore structure of tight sandstone were reconstructed; characteristic parameters for describing the pore morphology, such as shape factor, sphericity, and equivalent diameter, were defined; consequently, a new approach for quantitative classification of 3D pore morphology was proposed. This work achieves the fine characterization of the pore structure and quantitative classification of pore morphology for the tight sandstone, and lays the theoretical foundation for studying physical and transport properties of porous media in tight sandstone reservoirs (Cui et al., 2020; Wang et al., 2019).

Samples and experiments preparation

Sample collection

The study area mainly focuses on the Penglaizhen Formation in the Majing Gas Field and Xinchang Gas Field. The Majing Gas Field is situated on the northeastern footwall of the Pengxian Fault in western Sichuan Basin, characterized as a low-angle anticlinal structure (Li, 2009). The Xinchang Gas Field is located in the central Sichuan Basin, between the Zitong Syncline and Pengzhou Syncline (Chang, 2015). The Jurassic Penglaizhen Formation in the Xinchang area consists of a set of frequently interbedded sandstone and mudstone deposits with significant variations in individual layer thickness, ∼1250–1350 m in thickness. It exhibits a parallel unconformity with the overlying Cretaceous Jianmenguan Formation and conformable contact with the underlying Middle Jurassic Suining Formation. The nitrogen-measured porosity in the study area exhibits a range of 2.7%–21.4%, and is mainly distributed between 10% and 15% (Figure 1(a)). Similarly, the permeability displays a range of 0.0058 × 10⁻³–3.7992 × 10⁻³μm², and is mainly distributed lower than 1.00 × 10⁻³μm² (Figure 1(b)), which is a typical tight sandstone reservoir.

Figure 1.

Statistical diagram of (a) porosity and (b) permeability in the study area.

Experimental methods

CTS and SEM

According to SY/T 5162-2014, the sandstone samples for CTS were cut and polished into regular slices with a thickness of 0.03 mm and a diameter of 25 mm. The slices were impregnated with blue epoxy to highlight pores and then pasted onto glass slides for inspection. These thin sections were observed using an FEI optical microscope at the State Key Laboratory of Southwest Petroleum University. In addition, other samples were polished, gold-plated, and then placed in the Quanta 400 FEI SEM to investigate clay mineral characteristics and pore types.

X-ray diffraction

XRD has been applied widely to test the mineral composition and clay content. The crystal structure affects diffracted intensity. The relationship between the spatial orientation of diffraction lines and crystal structure is expressed by Bragg's equation: $2 d \sin θ = n λ$ , where λ is the wavelength, θ is the diffraction angle, d is the interplanar spacing, and n is an integer (Yu et al., 2011).

Micro-CT scanning imaging

X-ray CT, enabling non-destructive examination of the microstructure, has been widely applied to characterize the internal structure of tight sandstone (Liu et al., 2022). The MicroXCT-400 scanner from the State Key Laboratory of Southwest Petroleum University was utilized for micro-CT scanning. A total of three core samples with a diameter of ∼5 mm and a length of about 10 mm were selected for the high-precision X-CT scanning experiments, and the physical parameters are shown in Table 1.

Table 1.

Physical parameters of three sandstone samples for micro-computed tomography (CT) scanning in the Sichuan Basin.

Sample ID	Length (mm)	Diameter (mm)	Nitrogen-measured porosity (%)	Permeability (×er⁻³μm²)	Resolution (μm)
P1	10.52	5.94	13.49	1.0850	1.6137
P2	10.50	4.46	9.45	0.0830	1.3269
P3	10.52	5.88	10.51	0.0591	1.6132

The internal components of the CT scanning system consist of an X-ray source, a sample stage, and a detector. The X-ray source generates a highly controllable beam of X-rays. The tight sandstone sample placed on the stage can be rotated 360° with high precision and stability. The detector captures the attenuated X-rays that have penetrated the sample and converts them into digital signals (Andrew et al., 2014; Cnudde and Boone, 2013; Hu et al., 2025). Finally, the computer reconstructs a sequence of two-dimensional grayscale slices that represent the internal structure of the tight sandstone sample. These slices were stacked and rendered into a 3D volumetric model, thereby illustrating the spatial distribution of pores, throats, and mineral components clearly. Figure 2 illustrates the workflow of the 3D digital core model reconstruction process.

Figure 2.

The workflow of the three-dimensional digital core model reconstruction.

Methodology

Evaluation of porosity for digital core model

The porosity refers to the ratio of pore volume to the bulk volume of the rock sample (Zhu, 2014):

ϕ_{e} = \frac{\sum V_{p o r e}}{V_{b}} \times 100

(1)

where

ϕ_{e}

is the statistic porosity of the digital core model, %;

\sum V_{p o r e}

is the total pore volume, μm³; and

V_{b}

is the bulk volume of the digital core model, μm³.

Characteristic parameters of pore structure

In this article, characteristic parameters, such as pore–throat ratio $λ$ , tortuosity $τ$ , shape factor G, equivalent diameter $D_{p}$ , and sphericity $Ψ$ , were applied to the quantitative classification of pore structure for a tight sandstone sample.

(1) Pore–throat ratio: The pore–throat ratio is defined as the ratio of pore radius to the throat radius (Ye, 2008):

λ = \frac{r_{p}}{r_{t}}

(2)

where

r_{p}

is the pore radius, μm;

r_{t}

is the throat radius, μm; and

λ

is the pore–throat ratio, a dimensionless parameter. The larger the pore–throat ratio, the lower the permeability.

(2) Tortuosity: Tortuosity $(τ)$ is defined as the ratio of flow distance to displacement, representing a key parameter that quantifies the complexity of fluid through porous media. In the digital core model, tortuosity is defined as the ratio of the line length between different sections to the thickness of the section (Lin et al., 2018), and is calculated as follows:

τ = \frac{L_{d i s \tan c e}}{L_{d i s p l a c e m e n t}} = \frac{\sum_{i}^{k} l (n)}{\sqrt{{(x_{k} - x_{i})}^{2} + {(y_{k} - y_{i})}^{2} + {(z_{k} - z_{i})}^{2}}}

(3)

where

L_{d i s \tan c e}

represents the distance of fluid flow, and

L_{d i s p l a c e m e n t}

denotes the displacement of fluid flow; and

(x_{i}, y_{i}, z_{i})

(x_{k}, y_{k}, z_{k})

are the coordinates of any two nodes in rock space. The value of tortuosity is > 1. The more complex the porous media, the greater the tortuosity.

(3) Shape factor: The shape factor is used to characterize the complexity of pore morphology, which is calculated as follows (Li et al., 2019):

G = \frac{{(A_{3 D})}^{3}}{36 π {(V_{3 D})}^{2}}

(4)

where G is the shape factor, a dimensionless quantity;

A_{3 D}

is the area of a single pore, μm²;

V_{3 D}

is the volume of a single pore, μm³. For spherical pore,

A_{3 D} = 4 π R^{2}

and

V_{3 D} = \frac{4}{3} π R^{3}

. By substituting

A_{3 D}

and

V_{3 D}

into equation (4) gives:

G_{min} = 1

. The more complex the pore morphology, the greater the shape factor.

(4) Equivalent diameter: Due to the diversity of the pore morphology, it is difficult to characterize the single pore size. As such, the irregular pores of the sandstone sample are equivalent to a sphere of the same volume, and the diameter of the sphere is the equivalent diameter, which is calculated as follows (Zhu, 2014):

D_{p} = \sqrt[3]{\frac{6 V_{3 D}}{π}}

(5)

where

D_{p}

is the equivalent diameter, μm.

(5) Sphericity: Sphericity is used to characterize the degree of deviation of irregular pores from spherical pores, which is calculated as follows (Li et al., 2019):

Ψ = \frac{π^{1 / 3} {(6 V_{3 D})}^{2 / 3}}{A_{3 D}}

(6)

where

Ψ

is the sphericity, a dimensionless parameter, ranging from 0 to 1. For spherical pore,

A_{3 D} = 4 π R^{2}

and

V_{3 D} = \frac{4}{3} π R^{3}

, by substituting

A_{3 D}

and

V_{3 D}

into equation (6) gives:

Ψ_{max} = 1

. The more the pore shape deviates from a spherical pore, the smaller the sphericity.

Results and discussion

Reconstruction for 3D digital core model

Visualization of the digital core model

By integrating micro-scale CT scanning and image processing technology, including grayscale adjustment, filtering, and threshold segmentation (Zhao et al., 2025), 3D digital core models of a tight sandstone sample were reconstructed, as depicted in Figure 3(a) to (d). There is a significant difference in grayscale values between the pore and mineral regions in the pore matrix model; the gray areas represent the matrix components, the white areas represent dense minerals, and the blue areas indicate the pores of tight sandstone (Figure 3(a)). Subsequently, the combination of the watershed segmentation algorithm (Gostick, 2017; Wildenschild and Sheppard, 2008) with the top-hat algorithm (Bai and Zhou, 2010) is employed to obtain binary image segmentation. Ultimately, the pore and rock skeleton are identified, and the total pore model is obtained (Figure 3(b)). The pore morphology, spatial distribution, and pore connectivity are observed from the total pore model. Furthermore, removing the isolated pores from the total pores model, the spatial distribution of interconnected pores is obtained (Figure 3(c)), in which pore bodies and pore throat size distribution, pore–throat ratio, coordination number, shape factor, sphericity, and equivalent diameter are obtained. It lays a foundation for quantitative analysis of pore structure and quantitative classification of pore morphology (Wang et al., 2021; Yi et al., 2017).

Figure 3.

Three-dimensional (3D) visualization of the digital core model from micro-computed tomography (CT) scanning: (a) pore matrix model; (b) total pore model; (c) interconnected pore spatial distribution extracted from the rock matrix; and (d) skeleton model of the pore network based on the Maximal Ball algorithm.

On the basis of the interconnected pore model, the pore network model (PNM) is reconstructed by the Maximal Ball algorithm (Figure 3(d)). In this model, the pore bodies are simplified as small balls, and the pore throats are simplified as cylindrical sticks. As such, throat radius is determined by the cylindrical sticks bridging adjacent pores, thereby distinguishing pore throats from pore bodies. Ultimately, the interconnection of spheres with different sizes constructs the PNM (Dong and Blunt, 2009).

Pore connectivity analysis

Pore connectivity is a critical factor affecting the seepage capacity of tight sandstone reservoirs. To investigate the 3D connectivity and spatial distribution characteristics of the pore system, the total porosity, interconnected porosity, and isolated porosity of the digital core model for the three sandstone samples were analyzed (as shown in Table 2). The results indicate that sample P1 has good connectivity, with an interconnected porosity of 11.37% and an interconnected pore volume accounting for 88.34% of the total pore volume. In contrast, sample P2 shows poor connectivity, where the interconnected pore volume accounts for 77.26% of the total pore volume. Sample P3 has an interconnected porosity of 7.87%, with its interconnected pore volume representing 84.81% of the total pore volume. The average interconnected porosity of the three sandstone samples is 8.44%. The volume of interconnected pores accounts for 83.47% of the total pore volume, while the volume of isolated pores accounts for an average of 16.53% of the total pore volume. Furthermore, the total porosity of the digital core model for the three samples is a little lower than the nitrogen-measured porosity. This discrepancy is largely attributed to the limitation of resolution of the micro-CT scanning, ultimately causing systematic deviations in the geometric morphology and failing to identify nanopores (Liu et al., 2017). Specifically, samples P1 and P2 exhibit excellent permeability, resulting in smaller differences between the total porosity of the digital core model and the nitrogen-measured porosity, with errors of 4.6% and 1.75%, respectively. In contrast, sample P3 has poor permeability, leading to a larger discrepancy, with a porosity error of 11.7%.

Table 2.

The statistic porosity of the digital core model for three sandstone samples.

Sample ID	Nitrogen-measured Porosity (%)	Statistic porosity of digital core model (%)	Interconnected porosity (%)	Isolated porosity (%)	Fraction of interconnected pores (%)	Fraction of isolated pores (%)
P1	13.49	12.87	11.37	1.50	88.34	11.66
P2	8.01	7.87	6.08	1.79	77.26	22.74
P3	10.51	9.28	7.87	1.41	84.81	15.19
On average	–	–	8.44	1.57	83.47	16.53

The mineral components of tight sandstone samples

Reservoir petrological characteristics are a comprehensive integration of the features of the skeleton components, and they mainly include the mineral composition of rock debris particles, the characteristics of debris rounding, sorting, and arrangement (Han et al., 2024). As shown in Figure 4(a), the minerals in the study area are mainly composed of quartz, followed by feldspar, clay minerals, and calcite. The average contents of quartz, feldspar, clay minerals, calcite, dolomite, and pyrite are 47.6%, 20.4%, 15.0%, 13.5%, 3.2%, and 0.3%, respectively. The strong compaction effect leads to the secondary enlargement of quartz, resulting in the quartz crystals on the surface of intergranular pores and reducing pore channels. In addition, the content of feldspar is as high as 20.4%, and the dissolution effect caused by feldspar alteration can significantly improve reservoir quality and is responsible for the formation of high-quality reservoirs (Franca et al., 2003). Figure 4(b) illustrates that the clay minerals are dominated by mixed layers of green smectite, followed by illite, chlorite, and kaolinite. The average relative contents of chlorite mixed layer, illite, chlorite, and kaolinite are 65%, 18%, 8%, and 8%, respectively.

Figure 4.

The mineral composition content in the study area: (a) rock mineral composition; and (b) the relative content of clay mineral.

Pore throat types and the impacts on reservoir quality

Pore throat types based on pore genetics

Depositional environment and diagenetic alteration resulted in a diversity of pore throat types (Guan et al., 2024). As illustrated in Figure 5(a) to (c), the plastic particles undergo deformation because of the mechanical compression, and exhibit directional arrangement. In contrast, rigid particles, such as quartz and feldspar, form residual intergranular pores (Figure 5(d)). These pores have regular shapes, primarily triangular, quadrilateral, and polygonal, with clearly visible boundaries between the pores and particles. Their good connectivity contributes significantly to permeability (Zhu, 2020). Intercrystalline micropores are mainly formed by kaolinite filling and typically exhibit spherical or sheet-like shapes (Figure 5(e)). These pores have a limited ability to enhance the permeability of reservoirs. Changes in temperature and pressure conditions lead to physicochemical reactions between acidic components and soluble constituents within particles, resulting in secondary dissolved pores (Figure 5(f) to (i)). Feldspar dissolved pores are predominant and play a crucial role in improving reservoir quality. In the study area, there are three types of throats: necked, lamellar, and lamellar curved throats. Necked throats are primarily associated with point-to-point grain contacts. Lamellar and lamellar curved throats are products of compaction and cementation. Intensive compaction causes the particles to be in close contact with each other, either in a straight line or with concave–convex shapes, and thereby forming a tortuous pore system. Consequently, such complex diagenetic alterations lead to strong heterogeneity and anisotropy (Liu et al., 2022).

Figure 5.

Pore types of the Penglaizhen Formation in the Sichuan Basin were determined by SEM and thin section: (a) directional arrangement of flat particles; (b) point-contact among particles; (c) line-contact among particles; (d) primary intergranular pores; (e) intercrystalline pores; (f) intragranular dissolved pores; and (g)–(i) intergranular dissolved pores.

Pore throat size distribution

Figure 6 shows the distribution of pore throats and pore bodies for three samples obtained from a digital core model. It can be seen from Figure 6 that sample P1 exhibits a wide distribution size of pore bodies and pore throats. Specifically, the pore body sizes range from 2 to 65.36 μm, with a peak distribution between 11 and 21 μm, while the peak distribution of pore throat is distributed from 1 to 9 μm (Figure 6(a)). For the sample P2, the pore body sizes range from 1 to 37 μm, with a peak of 9–15 μm, and the pore throat sizes are distributed from 1 to 7 μm (Figure 6(b)). For the sample P3, pore bodies sizes range from 2 to 29 μm, with a peak of 4–12 μm, and pore throat sizes is distributed from 1.5 to 5 μm (Figure 6(c)).

Figure 6.

Radius distributions of three samples by micro-computed tomography (CT): (a) sample P1; (b) sample P2, and (c) sample P3.

Figure 7(a) shows the pore–throat ratio distribution of three samples. The pore–throat ratios of the three samples are mainly distributed between 2 and 7, accounting for as high as 69.68%. The smaller the pore–throat ratio, the lower the flow resistance, representing higher permeability. It is noteworthy that when the pore–throat ratio is 1, the fractions for samples P1, P2, and P3 are 15.84%, 11.39%, and 7.63%, respectively. Additionally, the average pore–throat ratios for samples P1, P2, and P3 are 3.83, 4.7, and 6.19, respectively.

Figure 7.

Statistic diagram of (a) pore–throat ratio and (b) pore–throat coordination number.

The pore–throat coordination number is defined as the number of cylindrical throats connected to a single spherical pore (Zhang et al., 2017). When the pore–throat coordination number is > 1, it means connected pores rather than isolated pores (Cheng et al., 2023). As shown in Figure 7(b), the distribution coordination of the three samples is basically consistent, mainly ranging from 1 to 5. The average coordination numbers of samples P1, P2, and P3 are 3.91, 3.36, and 3.71, respectively.

Pore morphology characteristics analysis

Pore morphology types

Pore classification has been extensively studied from several perspectives, such as genesis, size, and mineral composition, establishing a mature classification standard (Guan et al., 2024; Lai et al., 2018; Loucks et al., 2012). The study on pore morphology focused on the visually and qualitatively aspects. In this study, visual and quantitative classification of pore morphology were conducted. On the basis of the digital core model, pore morphology, such as spherical, triangular, polygonal, banded, and harbor-like, is extracted, as illustrated in Figure 8. Integrated analysis of CTS and SEM images reveals that spherical pores, typically characterized by small volumes and exits in isolation, contribute negligibly to reservoir permeability (Cai et al., 2021). Triangular and polygonal pores are predominantly residual intergranular pores, exhibiting high porosity and high permeability. Banded pores are mainly intergranular dissolution pores with large volumes and excellent connectivity, which enhance reservoir permeability. Harbor-like pores are mostly intergranular dissolved types with large volumes, and play a positive role in improving reservoir permeability (Zou et al., 2011).

Figure 8.

The main pore morphology type in the study area.

Characteristic parameters analysis

What the three core samples have in common that should be noted (Figures 9 and 10) are with the increase of the shape factor, the sphericity decreases, while the equivalent diameter increases. As shown in the “Characteristic parameters of pore structure’’ section, combining equation (4) with equation (6), the relationship between shape factor and sphericity is derived as $Ψ = G^{- \frac{1}{3}}$ . The results from Figure 9 are in good agreement with this formula. Figure 10 shows a good correlation between the shape factor and equivalent diameter, and the R-value is as high as 0.93. From the perspective of pore morphology, the results are explained that when the shape factor is small, the pores are mostly spherical or ellipsoidal in shape, resulting in a sphericity value close to 1. Besides, due to the small single pore volume, the equivalent pore diameter is also small. As the shape factor increases, the pore morphology becomes predominantly irregular, dominated by banded pores and harbor-like pores. These pores have characteristics of complex pore structures, larger single pore volumes, and good connectivity. Consequently, the sphericity value is < 1, and the equivalent pore diameter becomes larger.

Figure 9.

The relationship between the shape factor and sphericity: (a) sample P1; (b). sample P2; and (c) sample P3.

Figure 10.

The relationship between the shape factor and equivalent diameter: (a) sample P1; (b) sample P2; and (c) sample P3.

Quantitative classification of pore morphology

In this study, shape factor, sphericity, and equivalent diameter were employed for quantitative classification of pore morphology. Specifically, the shape factor is an important indicator and thus is taken as the dominant parameter of quantitative classification. Sphericity and equivalent diameter are taken as the secondary parameters. Using these parameters, pore morphology of tight sandstone reservoirs is clearly categorized, and the results of quantitative classification of pore morphology are shown in Table 3.

Table 3.

The results of quantitative classification of pore morphology in tight sandstone reservoirs

Sample ID	Pore morphology	Shape factor (–)	Sphericity (–)	Equivalent diameter (μm)
P1	Spherical pores	1–2.5	0.7368–0.9966	4.89–30.24
	Triangular pores	2.5–3.8	0.6788–0.7368	10.66–45.70
	Banded pores	3.8–5	0.5848–0.6933	15.62–63.02
	Harbor-like pores	>5	0.4311–0.5500	18.28–130.72
P2	Spherical pores and Triangular pores	1–4.2	0.6223–0.8216	13.40–56.64
	Banded pores	4.2–8	0.5083–0.6190	16.28–62.42
	Harbor-like pores	>8	0.3384–0.4999	19.44–74.32
P3	Spherical pores	1–2.25	0.7643–0.9040	4.94–30.20
	Triangular pores	2.25–2.8	0.6415–0.6404	8.52–41.90
	Banded pores	2.8–6	0.5665–0.6404	9.24–59.90
	Harbor-like pores	>6	0.3578–0.5660	18.04–57.98

Figures 11 to 13 show the results of the 3D pore morphology classification for three sandstone samples. It can be seen that the pore morphology has high similarity when the shape factor is within a certain range, which confirms the accuracy of the classification of pore morphology. It is noted that the shape factor differs a little among tight sandstones with different porosity and permeability. In other words, pore morphology has a high degree of diversity.

Figure 11.

The classification of pore morphology of sample P1: (a) spherical pores, and G is between 1 and 2.5; (b) triangular pores, and G is between 2.5 and 3.8; (c) banded pores, and G is between 3.8 and 5; and (d) harbor-like pores, and G is more than 5.

Figure 12.

The classification of pore morphology of sample P2: (a) spherical pores and triangular pores, and G is between 1 and 4.2; (b) banded pores, and G is 4.2 and 8; and (c) harbor-like pores, and G is more than 8.

Figure 13.

The classification of pore morphology of sample P3: (a) spherical pores, and G is between 1 and 2.25; (b) triangular pores, and G is between 2.25 and 2.8; (c) banded pores, and G is between 2.8 and 6; and (d) harbor-like pores, and G is more than 6.

Figure 14 illustrates the distribution frequency of pore number, pore volume, and surface area for samples P1, P2, and P3. For sample P1, pore morphologies are classified into spherical pores (1 ≤ G<2.5), triangular pores (2.5 ≤ G<3.8), banded pores (3.8 ≤ G<5), and harbor-like pores (G ≥ 5). The spherical pores and triangular pores account for 47.5% of the pore number and 9.05% of the pore volume. Their significant elongation length contributes to favorable pore connectivity. Banded pores constitute 47.49% of the pore number and 73.71% of the pore volume, while harbor-like pores account for 5.01% of the pore number and 17.24% of the pore volume. This phenomenon is attributed to the dissolution of feldspar, which generates banded and harbor-like pores characterized by irregular shapes and large single pore volumes. For sample P2, pore morphologies are categorized into spherical and triangular pores (1 ≤ G<4.2), banded pores (4.2 ≤ G<8), and harbor-like pores (G ≥ 8). Spherical and triangular pores dominated in pore number, while banded and harbor-like pores prevailed in volume. For sample P3, the pore morphologies are classified into spherical pores (1 ≤ G<2.25), triangular pores (2.25 ≤ G<2.8), banded pores (2.8 ≤ G<6), and harbor-like pores (G ≥ 6). Harbor-like pores account for 43.95% of the pore number and 77.86% of the pore volume, yet they represent a negligible fraction of the surface area.

Figure 14.

The fraction of spherical pores, triangular pores, banded pores, and harbor-like pores to the pore number, pore volume, and surface area of samples P1, P2, and P3.

Spherical pores and triangular pores make the greatest contribution in terms of pore count, while their contributions to pore volume and surface area are negligible. Banded pores and harbor-like pores together constitute the majority of pore volume, accounting for 91.35%, 80.64%, and 91.27% of the volume for the samples P1, P2, and P3, respectively, with an average of 87.75%.

Conclusions

In this study, the integration of digital core reconstruction technology and conventional petrophysical methods (XRD, SEM, and thin section) was employed to conduct visual and quantitative characterization of micropore structure in tight sandstone reservoirs in the Sichuan Basin. Specifically, based on CT and image processing technology, 3D digital core models were reconstructed. Additionally, the connectivity of pore structure and the classification of pore morphology were also discussed. The main findings can be summarized as follows:

The porosity is mainly distributed between 10% and 15%, and the permeability is mainly distributed lower than 1.00 × 10⁻³μm², which is a typical tight sandstone reservoir. The predominant pore types in the study area are primary intergranular pores, intergranular dissolved pores, intragranular dissolved pores, and intercrystalline pores, and the typical types of throats are necked, lamellar, and lamellar curved throats.

According to the digital core model, the pore structure of tight sandstone consists of both isolated pores and interconnected pores. The volume of interconnected pores accounts for 83.47% of the total pore volume, while the volume of isolated pores accounts for an average of 16.53% of the total pore volume. Additionally, the pore–throat ratio is mainly distributed between 2 and 7, and the coordination number is mainly distributed between 1 and 5.

Pore morphologies consist of spherical pores, triangular pores, banded pores, and harbor-like pores. Spherical pores and triangular pores contribute significantly to pore count but minimally to pore volume and surface area. In contrast, banded pores and harbor-like pores together constitute the majority of the pore volume (>80.64%) of the Penglaizhen Formation in the Sichuan Basin, representing the dominant morphological configuration within the reservoir.

The CT scanning-based micropore structure characterization method proposed in this article achieves comprehensive visualization and quantitative analysis of pore morphology in tight sandstone reservoirs. This advancement provides a technical foundation for further research on transport mechanisms in porous media of tight sandstone reservoirs.

Footnotes

ORCID iD

Xiaoming Su

Author contributions

Yuan Yuan: conceptualization, methodology, validation, writing–review and editing, visualization, supervision, and funding acquisition. Yingfeng Meng: validation, investigation, and writing–original draft preparation. Xiaoming Su: visualization and funding acquisition. Yawen He: visualization and project administration. Chao Li: data curation.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the 2023 Youth Fund of Lanzhou City University, 2023 Doctoral Research Fund of Lanzhou City University, Gansu Province college teachers Innovation fund (LZCU-QN2023-10, LZCU-BS2023-13, 2024B-142, and 2026A-171).

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

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