Insight into the Pore Structure of Tight Gas Sandstones - American

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Insight into the pore structure of tight gas sandstones: A case study in the Ordos Basin, NW China Hao Wu, Youliang Ji, Ruie Liu, Chunlin Zhang, and Sheng Chen Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/acs.energyfuels.7b01816 • Publication Date (Web): 16 Nov 2017 Downloaded from http://pubs.acs.org on November 27, 2017

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Insight into the Pore Structure of Tight Gas Sandstones: A Case Study in the Ordos Basin, NW China Hao Wua, Youliang Jia,*, Ruie Liub, Chunlin Zhangb, Sheng Chenb

a

State Key Laboratory of Petroleum Resource and Prospecting, College of Geosciences, China University of

Petroleum, Beijing, 102249, China b

Research Institute of Petroleum Exploration & Development, PetroChina, Langfang, 065007, China

*Corresponding author. Youliang Ji. E-mail: [email protected]; Tel: 010-89731636

Address: China University of Petroleum-Beijing, 18 Fuxue Road, Changping District, Beijing, 102249, China

Abstract: A wide spectrum of pore size distributions (PSD) exists in tight gas sandstones, ranging from several nanometers to several hundred micrometers in radius, which controls both the physical rock-flow capacity and storage capacity. Thin-section, scanning electron microscope (SEM), field emission scanning electron microscope (FE-SEM), high-pressure mercury intrusion (HPMI), constant-rate mercury intrusion (CRMI) and micro-CT scanning experiments are performed on tight sandstone samples from the eighth member of the Middle Permian Shihezi Formation (P2h8) in the Ordos Basin to better understand the pore-system characteristics of a tight gas sandstone. The results of this case study show that various types of pores exist in the P2h8 sandstones: residual intergranular pores, intraparticle dissolution pores, intercrystalline pores, and small micro-cracks are observed. We combine HPMI and CRMI to determine the PSD; the pore sizes range from 3.7 nm to 600 µm in

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radius. The multimodal PSD is characterized by two broad peaks. The right peak with radii between 50 and 600 µm acts as a pore body and is associated with residual intergranular pores and partial-dissolution pores in grains. The left peak, which corresponds to the throat, shows notable fluctuations and ranges from 3.7 nm to 50 µm; the pores within this size range are mostly associated with dissolution pores and intercrystalline pores. The permeability is mainly controlled by relatively large throats with a lower percentage. When the permeability is less than 1.0 mD, it is dominated by nanopores and micropores; in contrast, higher permeability is almost solely dominated by micropores. Additionally, nanopores are increasingly important in reservoir storage capabilities with decreasing permeability. A new empirical equation to estimate the permeability indicates that the pore throat radius of r30, which is the optimal representative for the permeability estimation of tight gas sandstones, generates the strongest correlation with the porosity and permeability. Key words: Tight gas sandstone; pore structure; pore size distribution; permeability estimation; Shihezi Formation; Ordos Basin

1. INTRODUCTION With the decline of conventional oil and gas production, tight sandstone gas is considered to be an important and the most realistic alternative resource to be developed on a large scale in China.1 The definition of tight sandstone is that its porosity is generally less than 12% and its air permeability is generally less than 1.0 mD, which is characterized by strong heterogeneity and poor reservoir quality.1-5 Exploration has confirmed that more than ninety percent of the sandstone permeability is less than 1 mD in the eighth member of the Middle Permian Shihezi Formation (hereafter called “P2h8”) in the Ordos Basin in NW China, which is interpreted to be typical tight

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sandstone reservoirs with approximately 1.1×1012 m3 of proven reserves of tight sandstone gas.1, 6 With the continuous exploration and exploitation of P2h8 tight gas sandstones, a microscopic pore structure in such low-porosity and low-permeability systems has long been a studied hotspot and major concern for petrophysicists and petroleum geoscientists because of its significant controls on the petrophysical properties of a reservoir.7-12 As indicated by previous studies,5, 13-18 the spectrum of the pore size distribution (PSD) of tight gas sandstone reservoirs is wide, ranging from several nanometers to several hundred micrometers, making characterizing the pore structure with routine methods difficult. In addition, the determination of the complete PSD in tight sandstones with only one method is more challenging because each method has its own limitations and strengths.19-20 Currently, a hybrid of methods is commonly used to investigate the pore geometry (pore-type, morphology, size and size distribution) of tight sandstones,8, 11, 12, 18, 20, 21 including a combination of fluid-invasion techniques (e.g., high-pressure mercury intrusion (HPMI), constant-rate mercury intrusion (CRMI), etc.) and radiation techniques (e.g., optical microscopy, scanning electron microscope (SEM), field emission scanning electron microscope (FE-SEM), nano/micro-CT, etc.).8,21 SEM and FE-SEM are often used to qualitatively observe the pore morphology and occurrence, but these methods fail to acquire quantitative pore size data.22-24 HPMI measures the distribution of the throat radius, where the exact count of large pores is inhibited by the shielding effect of small pores.25 However, HPMI is likely to yield the same capillary pressure curve with a different distribution of pore systems.26 CRMI, however, can overcome this pitfall, and its total capillary pressure curve can be divided into two subcurves: one represents the pore body capillary pressure curve, and the other calculates the throat capillary pressure curve.18, 26-28 Therefore, CRMI

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is an efficient technique to investigate the configuration of a relatively small throat and large pore body, which is typical for tight sandstone reservoirs.18, 29 Nevertheless, CRMI can easily access only the pores (radius > 0.12 µm) in reservoirs because the maximum entry pressure is approximately 6.2 MPa (900 psi). Although the micro-CT scanning technique is nondestructive and enables the pore network to be analyzed in a three-dimensional (3-D) image, its pervasive application is generally limited because of its high cost and limited resolution.30, 31 Micro-CT scanning previously failed to probe pores larger than 3 microns because of the experimental sample size, which may hold a high certainty for future uses.32, 33 In this research, a combination of the above methods was implemented to characterize the pore structure of the P2h8 tight gas sandstone reservoirs. Recently, various authors have shown that the PSD can provide a useful perspective to understand both the storage capacity and flow capacity of a reservoir, which in turn affects the porosity and permeability.5, 7, 14, 29, 34-36 Rezaee et al.5 investigated tight gas sandstones from the Western Australian region and suggested that both micropores and mesopores primarily contribute to the permeability. Lala and EI-Sayed35, 36 characterized both limestone and sandstone samples from Egypt and the Arabian Gulf using HPMI and concluded that macro-sized pore throats significantly contributed to the rock-flow capacity. Lala and EI-Sayed37 and Xi et al.29 studied the correlations between the reservoir storage capacity and flow capacity under different-sized pore throat intervals. Simaeys et al.38 reported that larger pore throat diameters generally resulted from an open pore network, which in turn reflects good reservoir properties. Although most studies have characterized the effect of the PSD in terms of the reservoir petrophysical properties, quantitatively characterizing the contributions from the PSD to the storage capacity and flow capacity of tight gas sandstones

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under a wide range of permeability values appears to be necessary. In addition, accurate permeability estimation has been a point of interest for many years because permeability is the preferred parameter when considering the economic value of hydrocarbon accumulation and production.5,

14, 39

Several techniques, such as HPMI and NMR, have been

implemented to establish models for the permeability estimation of sedimentary rocks in accordance with certain mathematical approaches, including regression analysis,5, 40 fuzzy logic,41 and artificial neutral networks,40 which associate the permeability with the porosity and pore throat size. Among them, the HPMI technique, which is combined with regression analysis to estimate the permeability, is the most commonly used technique. Winland42 developed an empirical relationship among the pore throat size, porosity, and permeability based on a simple assumption for sandstone with a simple pore structure. Pittman,43 Rezaee et al.,5, 40 Lala and EI-Sayed,35-37 and Hinai et al.44 extended Winland's equation and established a series of permeability evaluation formulas using a regression analysis for sandstones, carbonate rocks and shales. These studies documented many models of permeability estimation from mercury intrusion curves that are specific to conventional reservoirs to derive the absolute permeability,5,

40, 42, 43

but less attention has been paid to estimating the

permeability for unconventional oil and gas reservoirs, such as tight gas sandstone reservoirs.5 More importantly, permeability-prediction models that succeed in conventional reservoirs sometimes fail for tight reservoirs because of these features’ fine and complex pore structure. In this study, the pore structure of the P2h8 tight gas sandstone samples is characterized by using a series of experiment measurements, which are performed on the same source samples. The main aims of this paper are to (1) elucidate the pore types of tight gas sandstone reservoirs, (2) propose an

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integrated method to determine the PSD, (3) discuss the controls of the PSD on reservoir physical properties, and (4) establish an empirical equation to estimate the permeability for tight gas sandstones.

2. GEOLOGICAL SETTING The Ordos Basin, which is situated in northern central China (Figure 1), is a typical craton basin and is known as the second-largest sedimentary basin with rich coal, oil and gas resources.45-47 The basin is located between 34°00′-41°20′ N and 105°30′-110°30′ E with an area of approximately 25×104 km2, which is further divided into six first-class tectonic units (Figure 1). The study area in the Ordos basin, which is one of several tight gas enrichment areas, is located along the northern Shaanxi slope and is characterized by a gentle monoclinic structure with a dip angle of approximately 1°, and internal fractures are undeveloped.45, 48, 49 The Permian order from bottom to top in the study area includes the Lower Permian Taiyuan Formation (P1t) and Shanxi Formation (P1s), the Middle Permian Shihezi Formation (P2h) and the Upper Permian Shiqianfeng Formation (P3q) (Figure 2). The Permian stratigraphy includes many reservoir rocks and source rocks with potential for hydrocarbon accumulation (Figure 2). The investigated interval, P2h8, comprises thick sandstones that are intercalated with thin layers of mudstones (Figure 2). The P2h8 tight gas sandstone reservoirs are mainly distributed in a braided fluvial channel.48, 50, 51 The exploration history and previous studies have suggested that gas source rocks comprise the Taiyuan Formation and Shanxi Formation’s coal seams and carbonaceous mudstones45, 48, 51, 52 and that the P2h8 sandstone reservoirs were tight before natural gas began to massively charge, which occurred during the Late Jurassic–Early Cretaceous period.48, 50

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3. SAMPLES AND METHODS 3.1. Sample Preparation

A total of nine core samples that cover a major range of present-day burial depths and permeability were collected from the P2h8 tight gas sandstones in the Ordos Basin, NW China. All the core samples were medium- to coarse-grained sandstones and rich in quartz48,

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with a

sub-dominant component of lithic fragments and low feldspar (Table 1), which are classified as sublitharenite to quartzarenite. The clay types of tight sandstones are dominated by kaolinite, while mixed I/S layers and chlorite are relatively less dominant.46, 54 Compaction, silicate, carbonate, and clay cements are the main diagenetic behaviors that have affected the petrophysical parameters of the tight sandstone samples. Cylindrical core plugs (diameter = 2.54 cm, length = 5 cm) were cut perpendicularly from the main large core samples from the wellbore, i.e., parallel to the bedding plane, and were analyzed through a series of experiments. Before these experiments, each core plug was cleaned with a mixed solution of alcohol and trichloromethane and was dried with a vacuum at 110°C for 24 h. First, porosity and permeability tests were directly performed on each cylindrical core plug, and then the sample plugs were split into several sub-plugs for thin-section petrography, SEM, FE-SEM, micro-CT scanning, HPMI and CRMI experiments. The HPMI and CRMI techniques were employed to run tests on each sample in a predefined series, and samples Ss59 and Ss100 were selected for micro-CT scanning to compare the results from HPMI and CRMI, which were performed on the same source samples.

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3.2. Experimental Methods

The porosity and permeability of nine samples were measured using the CMS-300 core measuring instrument following the People's Republic of China Petroleum and Natural Gas Industry Standard (SY/T) 5336-1996. The helium expansion method was utilized to determine the porosity. The permeability was measured using the pressure-transient technique, and helium was used as the measuring medium. Casting thin-sections, which were constructed through vacuum-pressure impregnation with red epoxy resin, were observed to analyze skeleton particles, intergranular pore-filling minerals and the pore types of the investigated sandstones.55 Point counts were also used to determine the amount of detrital components and grain size with more than 350 points for each thin section, which could ensure a standard deviation of approximately 6% or less.55, 56 An FE-SEM analysis with 0.1-nm- to micron-grade ultra-high resolution was performed using a Quanta 200FEG device. The samples, which were cut to 2 mm in diameter and 1 mm in thickness with 10 nm-thick carbon films, were evaluated at a temperature of 24°C and a humidity level of 35%, and FE-SEM micrographs could realistically provide important information on the pore morphology, occurrence and connectivity.23 Additionally, some gold-coated sample chips were used with a JEOL JSM 840A SEM device at an accelerating voltage of 20 kV and a current emission of 50–100 pA with a back-scattered electron detector following the SY/T 5162-1997 standard. The HPMI experiment was conducted by using an AutoPore IV9500 mercury porosimeter. Nine samples (diameter = 2.54 cm, length = 2.5 cm) were measured. Once the injection pressure reached the maximum, the pressure began to progressively decrease, accompanied by mercury ejection from

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the samples. Both the injection and ejection capillary pressure curves were acquired. The equivalent pore radius r was calculated according to the Washburn equation (Eq. (1)).57 r (m) = -2γcosθ/Pc

(1)

where r is the radius of the corresponding cylinder, Pc is the capillary pressure (Pa), θ is the contact angle (°), and γ is the interfacial tension (N/m). Assuming an interfacial tension of 0.485 N/m and a contact angle of 140°,58 the injection pressure ranges from 0.004 to 200 MPa, corresponding to a pore radius of ~177.8 µm to ~3.7 nm. CRMI measurements were conducted by using an APSE-730 mercury porosimeter (American Coretest Systems, Inc.). CRMI was executed at a quasi-static constant speed of 0.00005 ml/min. By detecting the pressure fluctuation during the mercury injection process (Figure 3), the pore body could be identified from sudden decreases in capillary pressure and the throat could be distinguished from increasing capillary pressure.26, 27 The total capillary pressure curve could be partitioned into two sub-curves, namely, the pore body capillary pressure curve and the throat capillary pressure curve, which detail the pore body and throat size distribution, respectively.26 The pore body was calculated as a radius of equivalent spheres,18, 26, 27, 59 and the throat radius was computed according to the Washburn equation.57 Nine sister samples (diameter = 2.54 cm, length = 0.5 cm) were measured, and their pore body, throat radius and pore body to throat ratio were obtained. The maximum injection pressure of the CRMI was 6.2 MPa to keep the intrusion rate quasi-static; the corresponding pore radius was approximately 0.12 µm. Micro-CT scanning, which is a nondestructive technique, provides a 3-D image at a resolution of several microns during pore-structure characterization.32,

33, 60, 61

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The CT image reflects the

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information of the energy attenuation of the X-ray as it penetrates the sample. The attenuation decreases with decreasing sample density, which is associated with the sample composition and inner structure.62 Here, 3-D images were constructed from the 2-D tomographic cross-sections that were obtained by using the back-projection algorithm. Image segmentation is a key step to eliminate artifacts and outline the pore system and different mineral phases.32,

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A sphere maximization

algorithm is commonly implemented to extract the pore body and throat from tomogram images by probing for spheres with different diameters in the pore system.33, 63 The pore system was visualized by overlapping spheres with different diameters, and the simplified sphere clusters were implemented to describe the networks of pore body and throat units (Figure 4a). The configuration relationship of the largest sphere and the smallest spheres among two regional maximum spheres was interpreted as a pore body−throat−pore body set (Figure 4b). The details of the processing methodology of micro-CT have been thoroughly explained by Dong and Blunt.32 The images of the 3-D models were constructed from the network and segmentation output of the Avizo software.64 In this work, two samples of different rock types that were selected for HPMI and CRMI experiments were also prepared for a 3-D pore network analysis with the micro-CT system Phoenix nanotom m (Manufacturer, GE Sensing & Inspection Technologies GmbH, Wunstorf, Germany), which was operated at 90 kV and 100 µA. Sub-plugs that were 5 mm in diameter were drilled from the 2.54-cm-diameter core plugs for the micro-CT tomogram images (2048 × 2048 pixel2), with an image resolution of approximately 3 µm. The number, diameter, and volume of the pore body and throat, the pore connectivity and the visual 3-D images were characterized with this method.

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4. RESULTS 4.1. Porosity, Permeability and Pore Types

Helium porosity and Klinkenberg-corrected permeability data from the petrologic analysis showed that P2H8 generally exhibited poor reservoir physical properties (Table 1). The porosity of the samples varied from 6.8% to 15.1% and averaged 9.63%. The permeability of the samples apparently changed from 0.321 mD to 5.651 mD. Thin-section, SEM and FE-SEM observations showed that the various pores in the P2h8 tight gas sandstones mainly included the following basic types: residual intergranular pores; secondary pores, including intraparticle dissolution pores; intercrystalline pores; and small micro-cracks. Intergranular pores were observed as the remaining pore space after diagenesis,23 where cements such as quartz and carbonate had incompletely filled the pores between rigid grains such as quartz (Figure 5a, b). Some of these pores may have contained authigenic quartz and clay-mineral cements, including illite and chlorite (Figure 5c, d), which blocked the pore space. Intergranular pores were commonly triangular in shape (Figure 5a-c), and the long dimensions of these pores were typically > 50 µm. The dissolution of pore-filling cements such as calcite rarely occurred in these tight gas sandstones (Figure 5e, g), so dissolution pores generally resulted from intraparticle dissolution, such as feldspar and lithic fragments (Figure 5e-h).53 Extensive feldspar dissolution pores generally developed along cleavage planes (Figure 5e-g). The intraparticle dissolution pores ranged in size from approximately 2.2 to 400 µm (Figure 5e, g, h). The shapes of these pores were generally associated with their origin and dissolution degree.65 Intraparticle pores that resulted from the complete dissolution of grains took the shape of the host and precursor minerals (Figure 5e, h). Intraparticle pores with partial

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dissolution were commonly irregular (Figure 5e, g), but feldspar dissolution pores were elongated and appeared to exhibit preferential orientation along cleavage planes (Figure 5f). Although the dissolution pores that occurred along the edges of grains were commonly indistinguishable from intergranular pores, both types of pores were conducive to the total porosity (Figure 5a, e). Here, “intercrystalline pores” mainly refers to pores within biotite (Figure 5i) and clay aggregates. Typical clay minerals in the P2h8 sandstones were lamellar chlorite (Figure 5j), pseudo-hexagonal platy kaolinite (Figure 5k) and fibrous mixed illite-smectite layers (Figure 5l), with pore sizes that were often less than 10 µm. Intercrystalline pores had various shapes that were controlled by the occurrence and arrangement of crystals and included sheet-like, angular, elongated, and rounded shapes (Figure 5i-l), which was consistent with the results by Milliken and Reed (2010)66 and Loucks et al. (2012).65 Because of the intense compaction and cementation, the pore type was dominated by

secondary pores, followed by residual intergranular pores.46 Particles in the samples largely exhibited in line-line contact (Figure 5a, e, g), so curved-laminated throats between particles and tubular throats in clays were the main throat types in the P2h8 tight gas sandstones.

4.2. HPMI Results

The HPMI capillary pressure curves of nine samples are shown in Figure 6, and detailed characteristic parameters are tabulated in Table 2. The nine samples could be categorized into two types based on the characteristics of the capillary pressure curves (Figure 6). Type I, including samples Ss61, Ss100 and Sn247, exhibited a relatively lower threshold entry pressure (Pte) (the point on the injection curve where mercury first entered the sample29), ranging from 0.18 to 0.29 MPa with an average of 0.26 MPa (Table 2). The injection curves were characterized by a relatively steep

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curve configuration with no notable flat segments. For the type II samples, the Pte ranged from 0.45 to 1.14 MPa with an average of 0.70 MPa (Table 2). The sorting of pore throats was relatively better, as indicated by the well-developed flat segment in the injection mercury curves between an entry pressure of 1 MPa and 4 MPa. Moreover, the Pte of type II was higher than that of type I, and the Pte appeared to be negatively correlated with the permeability (Table 2): as the permeability increased, the Pte became smaller, which also indicated that the type-I samples had a relatively larger pore throat system than the type-II samples. Compared to the type-II samples, both the maximum mercury saturation (Sm) and residual mercury saturation (Sr) showed some differences among the type-I samples (Table 2). The Sm ranged from 81.83 to 93.69% (avg. 86.77%), and the Sr varied between 47.13 and 70.28% (avg. 61.81%). Sample Ss59 exhibited an Sm of 93.69%, while its Sr decreased to 47.13%, indicating a large pore body-throat discrepancy and explaining why quantities of mercury were stranded in the pores.18, 25 The PSDs of nine samples were calculated based on the mercury injection curve and Washburn equation57 (Figure 7). All the samples exhibited a relatively wide and multimodal PSD, varying between 3.7 nm and 10 µm in radius. A distinct discrepancy between the two types existed that was specific to the PSD. For the type-I samples, the pore radius showed bimodal-distribution characteristics (Figure 7a). The first PSD peak covered radii of 3.7 nm–0.1 µm, with a mode of approximately 0.01 µm. The second peak spanned a pore radius of 0.1–10 µm, with a mode of 1 µm. For the type-II samples, however, the PSD was notably unimodal, and their pore sizes ranged from 3.7 nm to 1 µm in radius, with a mode of 0.2 µm (Figure 7b). The pores with a radius < 0.2 µm displayed significant fluctuations, indicating that multiple types of pores existed in the tight gas

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sandstones.67 Overall, pores with a radius > 10 µm were rare, which did not match the petrographic results. Previous studies indicated that the shielding effect of large pores by small accessible pores may cause this discrepancy.25, 68

4.3. CRMI Results

The CRMI-derived capillary pressure curves of the samples shared similar characteristics, which was illustrated by samples Ss59 and Ss100 (Figure 8). The trend of the total mercury injection curves followed the trend of the pore body mercury injection curve at the initial stage. With increasing entry pressure, the mercury saturation of the pore body rapidly increased and occurred only in a narrow entry pressure range, while the mercury saturation of the throat continuously increased, suggesting that the pore bodies that were characterized by CRMI were generally connected by a small proportion of relatively large throats.69 The Pte of the samples ranged from 0.09 to 0.53 MPa with an average of 0.25 MPa, showing a negative correlation with the permeability: as the permeability increased, the Pte decreased, as shown in samples Ss61 and Ss315 (Table 2). The pore body mercury saturation of the samples ranged from 6.72 to 59.51% (avg. 24.53%), and the throat mercury saturation ranged between 26.37 and 46.69% (avg. 33.32%). Moreover, the Sm of the samples varied from 32.55 to 85.88% with a mean of 57.85%, which was lower than that of the HPMI (avg. 86.77%). The maximum applied injection pressure of the HPMI and CRMI was 200 MPa and 6.2 MPa, respectively; thus, only pores that were larger than 3.7 nm and 0.12 µm in radius could be characterized, respectively. Therefore, this significant difference in the Sm between the HMPI and CRMI resulted from the limitations of the experimental conditions. Figure 9 shows the size distribution of the pore body, throat and pore body to throat ratio for all

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the samples. The pore body radius among all the samples with different permeabilities exhibited similar distribution characteristics and small variations, indicating that the pore body sizes might not have been the factor that controlled the reservoir permeability, in contrast to the throat radius.29 A pore body radius of approximately 100–200 µm exhibited an important proportion, with a peak value of ~140 µm (Figure 9a). The throat size distribution ranged from 0.12 to 10.95 µm in radius and mainly fell within the range of 0.2–2 µm, which was supported by Nelson14 and Rezaee et al.5 Moreover, the throat size distribution significantly differed among the samples: as the permeability value increased, the throat size distribution became wider (Figure 9b). For samples with a high permeability value, such as Sn247 (2.974 mD), the throat radius distribution was wider and ranged from 0.12 to 10.95 µm, with a mean of 4.74 µm. However, in samples with a low permeability value, such as Ss59 (0.405 mD), the throat radius distribution was relatively narrow and varied between 0.12 and 5.83 µm with an average value of 0.79 µm. The pore body to throat ratio, which is defined as the ratio of the pore body radius to the throat radius, also showed a wide range and apparent variations among the samples, with the size ranging between 10 and 1000 (Figure 9c). The mean pore body to throat ratio for the nine selected samples ranged from 52.96 to 208.17 with a mean of 135.9 (Table 2). In addition, the pore body to throat ratio had a small correlation with the porosity (Figure 10a) but decreased markedly with increasing permeability (Figure 10b). The relationship between the mercury saturation and PSD of all the samples is shown in Figure 9d. The PSD was characterized by two main peaks. The peak to the right mainly ranged from 100 to 600 µm, whereas the peak to the left mainly ranged from 0.12 to 10 µm. The mean CRMI Sm of all the samples was 57.85%, and the residual pore volume consisted of two portions, i.e., throats that

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were less than 0.12 µm in radius and pore bodies that were controlled by these throats, which could be probed by combining CRMI with other methods, such as HPMI.

4.4. Micro-CT Results

Two typical samples, namely, Ss100 and Ss59, were examined, and their 3-D images of a ball-and-stick model of a pore body-throat, the pore body network and throat network are illustrated in Figure 11 and Figure 12. The connectivity and sizes of pores were detected as being directly specific to the 3-D images. The results of the 3-D visualization of the pore body and throat networks were characterized by different colors, with the rock matrix in black. In Figure 11 and Figure 12, different color codes were used to delineate the pore connectivity, i.e., the same color indicated mutually interconnected pores, whereas different colors implied that the pores were either isolated or disconnected. Significant differences were found between Ss100 and Ss59. For sample Ss100, which had high permeability (1.739 mD), the pore-color types were few and a number of pores were mainly displayed in orange, dark blue and light blue (Figure 11c), suggesting that sample Ss100 had relatively better pore connectivity (Figure 11d). A few pores in other color codes, such as green and purple (Figure 11c), were regarded as isolated pores or disconnected from the major pores because no effective throats existed between these pores (Figure 11d). Compared to sample Ss100, Ss59 was notably tighter, with a permeability of 0.405 mD. These pores were sparsely distributed and coded in diverse colors, and pores of the same color were sporadically distributed throughout the pore system (Figure 12c), implying relatively poor connectivity within sample 59 (Figure 12). The pore body and throat size distributions that were extracted from the corresponding 3-D volume data are shown in Figure 13. The pore body sizes were approximately 100–300 µm in radius

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(Figure 13a), which was consistent with the petrographic and CRMI results. Figure 13b shows that the throat size distributions for the selected samples according to different methods and as revealed by micro-CT scanning did not match the CRMI and HPMI results. Throats with radii above 3 µm were characterized by micro-CT scanning but were rare in the CRMI and HPMI results (Figure 13b). Three factors may explain this inconsistency. 1) The determination of the throat size distribution via micro-CT scanning is constrained by the maximum resolution, which is the most important reason why throats that are smaller than 3 µm in radius cannot be characterized.70 2) CRMI and HPMI measure only accessible pore systems, whereas micro-CT scanning can also measure inaccessible pore system. As shown in Figure 11 and Figure 12, a large number of isolated throats existed in the throat network, which may have been the main cause of this discrepancy. 3) When a pore space is segmented into pore bodies and throats based on pore body-throat chains, the definition of a pore body or throat is somewhat arbitrary, which may have created a certain deviation in the results.32, 33 Although micro-CT scanning appears reasonable to characterize relatively large pores, the inability of this method to estimate the number of pores < 3 µm makes this method insufficient to describe the throat networks of tight gas sandstones. However, we can improve the maximum resolution by reducing the sample size.71, 72 For instance, the maximum resolution of a sample with a 1-mm diameter in micro-CT scanning can reach 0.52 µm. With a maximum resolution of 50 nm, nano-CT scanning can also be used to characterize pores that are larger than 50 nm in radius, but its wide-range application is often restricted by expenses.

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5. DISCUSSION 5.1. Proposed Approach to Characterize the PSD

As discussed above, HPMI, CRMI and micro-CT experiments were conducted by using the same samples, but no sole method could be used to determine the total PSD because of the various types and scales of the pores in the P2h8 tight gas reservoirs. HPMI could measure relatively small pores but could not quantify relatively large pores, i.e., > 40 µm. CRMI could characterize pore structure with a relatively large pore body to throat ratio but had a lower pore radius limit of approximately 0.12 µm. Because of the resolution that was applied, the throat size distribution that was detected by micro-CT scanning was slightly larger than those by CRMI and HPMI, whereas the pore body size distribution matched the CRMI and petrographic results. In contrast, HPMI and CRMI followed the same physical procedure of mercury intrusion;18, 26, 27 i.e., the HPMI and CRMI methods measured only accessible, interconnected pores,20,

26, 58

whereas micro-CT scanning could also measure

inaccessible pores. Interconnected pores are crucial controls of the effective porosity and flow properties of tight sandstones.7, 10, 34, 73 Therefore, we propose integrating the HPMI and CRMI methods to elucidate the effective (connected) full-range PSD of tight gas sandstones. A comparison between the HPMI and CRMI results for samples Ss59 and Ss100 revealed a small discrepancy between the injection curves (Figure 14a, b). Under the same capillary pressure, the mercury saturation of CRMI was always higher than that of HPMI. The reasons for this discrepancy might be related to the following. (1) The injection velocity for CRMI is a quasi-static constant value that keeps the interfacial tension and contact angle constant. However, the high injection velocity in HPMI may make the contact angle change, creating an inconsistency.18 (2) With regard to pore

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geometries, such as slit pores, some pores may have a dual role with both pore bodies and throats.73 In such cases, HPMI and CRMI analyses should theoretically yield similar results. Therefore, discrepancies in certain pore size ranges may be caused by deviations from slit-shaped pore geometry. Additionally, some scholars suggested that the compressibility effects between detrital grains at the high injection pressure of HPMI can cause the capillary pressure curves to shift.18, 21, 29 However, the experimental results indicated that the compression of grains was more pronounced in weaker materials such as activated charcoal and cellulose.68, 74 The samples of the investigated P2h8 reservoirs were quartz-rich sandstones, which are much stronger than charcoal. Therefore, the required elevated pressures for mercury injection may have made the capillary curves of the investigated samples much less susceptible to shifting.74 The integration of HPMI and CRMI is regarded as an effective approach to characterize the entire interconnected PSD of tight gas sandstone reservoirs despite the discrepancies that exist between the two techniques. When HPMI and CRMI data from the same sample were merged, the PSD ranged between 3.7 nm and 600 µm in radius (Figure 14c). In addition, the overlapping pore size range from both HPMI and CRMI was concentrated within 0.12–10 µm because pores > 10 µm in radius were rare. Figure 14c illustrates the multimodal PSD of the samples, which was characterized by two broad peaks that exhibited multiple scales of porosity. The right peak, which was measured by CRMI alone, represents pores with radii between 50 and 600 µm, which are associated with residual intergranular pores and partial-dissolution pores in grains. The left peak covers 0.12–50 µm and is characterized by a combination of HPMI and CRMI data. Pores of these sizes are associated with dissolution pores and a portion of intercrystalline pores. Pores in the range

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of 3.7 nm–0.12 µm that were measured by HPMI alone are associated with intercrystalline pores between clay-mineral aggregates, as proved by Nelson.14 As noted by Clarkson et al.,73 HPMI actually produces information regarding the throat distribution instead of the pore body distribution. Therefore, the left peak from both HPMI and CRMI should represent the throat size distribution, whereas the right peak should display the pore body size distribution that shows similar distribution characteristics (Figure 14). The porosity and permeability of sample Ss59 in the core plug test were 10.9% and 0.405 mD, respectively, and these values for sample Ss100 were 10.6% and 1.739 mD, respectively (Table 1). The porosity, therefore, was similar in value between the two samples, whereas the permeability notably deviated in value. This difference could be attributed to the different throat size distributions and similar pore body size distributions of the samples (Figure 14c). The throat size mainly varied between 3.7 nm and 1 µm with a main peak value of 0.3 µm for sample Ss59, and between 3.7 nm and 3 µm with a peak value of 1 µm for sample Ss100, which resulted in a higher pore body to throat ratio of Ss59 than that of Ss100 (Table 2). As mentioned above, the strong correlation between the pore body to throat ratio and permeability (Figure 10b) may be controlled by the throat size distribution,44, 75 which can significantly affect the permeability of tight gas sandstone reservoirs. Pores are classified as nanopores (< 0.5 µm in radius), micropores (0.5–30 µm), mesopores (30 µm–2 mm), and macropores (> 2 mm) following the size classification scheme by Loucks et al.65 The PSD for the investigated samples ranged from approximately 3.7 nm to 600 µm, therefore falling within the realms of nanopores, micropores and mesopores (Figure 14). Mesopores mainly acted as storage space, while both nanopores and micropores were the main features that contributed

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to the permeability.

5.2. PSD Controls on Physical Properties of a Reservoir

As discussed above, the PSD is a crucial factor that affects the physical properties of a tight sandstone reservoir. Both the incremental mercury injection saturation and cumulative mercury injection saturation, which reflect the pore volume or storage capability of a reservoir, can be directly obtained from HPMI measurements. According to the transformation of the Kozeny-Carman equation,76, 77 the incremental permeability contribution ( ∆K i ) and the cumulative permeability contribution ( Ki ), which disclose information on the percolation potential of a reservoir, can be calculated using the following equations in order: 2

∆K i =

Ki =

ri ∆ S i n

(2)

2

∑ i =1 ri ∆ S i ∑

n i =1

(3)

∆K i

where ri is the pore throat radius (microns) and ∆Si is the incremental mercury injection saturation that corresponds to the pore throat radius ri (%). The constructed relationships between the pore throat sizes and storage capability and percolation potential of the studied tight gas sandstones are shown in Figure 15. The tendency of the cumulative permeability contribution curves of samples with different permeability levels was steep and almost reached the maximum at the early phase of injection (Figure 15), suggesting that the permeability was mostly controlled by relatively large throats with a lower percentage. The cumulative mercury injection saturation curve, however, continuously increased throughout the injection until the highest saturation was achieved (Figure 15), indicating that various types and

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scales of pores controlled the storage capability of the samples. In addition, the pore throat peak value of the permeability contribution curve showed a positive correlation with the permeability (Figure 16a): as the permeability decreased, the pore throat peak value moved to the left and decreased; the larger the permeability value became, the greater the number of connected larger pore throats existed in the samples. We set a pore throat radius of 0.5 µm as a cutoff value to quantitatively investigate the effects of the PSD on the physical properties of tight gas reservoirs. The calculation procedure to determine the cumulative contributions from the PSD to the storage capability and percolation potential is demonstrated in Figure 15c. First, we began at 0.5 µm on the X-axis and moved vertically to intersect the storage capability curve and percolation potential curve. Second, two lines were drawn parallel to the X-axis at the intersections, and the intersections of the horizontal lines with the Y-axis from bottom to top denoted the cumulative mercury injection saturation and cumulative permeability contributions from pore throats that were larger than 0.5 µm. The rest were dominated by contributions from pore throats that were smaller than 0.5 µm. The cumulative contributions from the PSD to the storage capability and percolation potential for all the studied samples as calculated by the above method are tabulated in Table 3. For samples with a permeability below 1 mD, the contributions from pore throats that were larger than 0.5 µm to the porosity and permeability ranged from 32.0 to 46.5% (avg. 18.5%) and from 58.5 to 73.9% (avg. 68.1%), respectively, and the values for pore throats that were smaller than 0.5 µm varied from 61.8 to 82.7% (avg. 68.3%) and from 27.0 to 41.5% (avg. 31.9%), respectively. Approximately 18.5% of the storage capacity or pore volume (read on the solid blue curve) contributed to 68.1% of the flow capacity of the sample (read on the

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solid red curve) (Figure 15a, b), suggesting that the percolation potential of the samples was affected by both nanopores and micropores (Figure 17). In addition, the contribution from nanopores to the reservoir porosity was significantly greater than that from micropores (Figure 17a). For samples with a permeability above 1 mD, the contributions from pore throats that were larger than 0.5 µm to the porosity and permeability ranged from 32.0 to 46.5% (avg. 37.4%) and from 97.3 to 98.8% (avg. 98.1%), respectively, and the values for pore throats that were smaller than 0.5 µm varied from 42.1 to 53.7% (avg. 50%) and from 1.2 to 2.7% (avg. 1.9%), respectively. Approximately 37.4% of the storage capability or pore volume contributed to 98.1% of the percolation potential of the samples (Figure 15c, d), which was mainly controlled by the micropores (Figure 17b). Nanopores had a small influence on the reservoir permeability (Figure 17b) but may have played an important role in contributing to the reservoir porosity (Figure 17a). Nanopores were increasingly important to the reservoir storage capability with decreasing permeability (Figure 16b), especially for samples with a permeability below 1 mD.

5.3. Permeability Estimation for Tight Gas Sandstones

Several models have been established to estimate the permeability of sandstone reservoirs based on pore throat sizes, as determined by the mercury intrusion capillary pressure. Commonly used models such as the Winland model,42 Pittman model,43 and Rezaee model5 are listed in Appendix A. First, the estimated permeabilities of the P2h8 tight sandstone samples were computed using the Winland, Pittman and Rezaee formulas, which underestimated the permeability and showed poor correlation with the measured core plug permeability (Figure 18a). These results indicated that these empirical equations were not suitable for the permeability evaluation of the investigated tight gas

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reservoirs. Therefore, we conducted eleven groups of regression analyses with 5% mercury saturation intervals from 10 to 60% to determine the optimal pore throat radius, i.e., the radius with the strongest correlation with the permeability. These multiple linear regression analysis results are provided in Table 4. The pore throat radius (r10–r35), which corresponds to 10–35% mercury saturation, correlated well with the permeability, and all the correlation coefficients R2 were greater than 0.9 (Table 4). The highest correlation coefficient of 0.9535 was generated by r30 and therefore was the best permeability predictor for the studied tight gas reservoirs. The corresponding empirical equation is expressed as follows: log k = 0.321 + 1.478log r30 + 0.164log φ

(4)

where φ and k represent the porosity (%) and permeability (mD), respectively, and r30 is related to the radius of the pore throat (µm), which corresponds to the 30th percentile of mercury saturation on the cumulative mercury curve. The permeability values that were estimated using the new equation with r30 matched the measured core plug permeability values (Figure 18b). Neither the Winland nor Pittman permeability equations, which are specific to normal sandstones, are suitable for tight gas sandstones because the pore structures of normal sandstones are notably different from those of tight sandstones. In addition, the permeability values that were estimated from the Rezaee equation, which was established for tight gas sandstone reservoirs, poorly estimated the permeability of the P2h8 tight gas sandstone reservoir (Figure 18a), which suggests that the pore network complexity of different tight sandstones can also differ.44 Different from the newly proposed equation in this paper, Rezaee (2012)5 proposed r10 as the best permeability predictor for tight gas sandstone. This factor caused the permeability

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values that were predicted from the Rezaee equation to deviate from the 1:1 trend line (the black dashed line in Figure 18), and the degree of deviation became more marked for samples with lower permeability. The contribution from the pore throat radius r10 to the permeability for samples with lower permeability, such as Ss120, was 44% (Figure 15b), while the value for samples with greater permeability, such as Sn247, reached 63% (Figure 15d). As the permeability of the samples increased, the calculated permeability from the permeability estimator r10 moved closer to the 1:1 trend line (Figure 18a). The pore network complexity of a reservoir is the end result of a correlation between the initial depositional conditions (e.g., rock composition and texture) and subsequent diagenetic processes.38, 65, 78 In contrast, we found that the mineralogy may be the genetic driver for the pore network differences between the tight gas reservoirs that were investigated by Rezaee and the P2h8 tight gas reservoirs. The quartz content of the P2h8 tight gas reservoirs is much higher than that of the tight gas reservoirs that were investigated by Rezaee,5 which may have affected the pore structure by influencing diagenesis78 and therefore creating diverse pore throat radius ranges that dominated the flow.

6. CONCLUSIONS In this study, the pore structures of nine samples from the P2h8 tight gas sandstones in the Ordos Basin were probed through a series of experiments. The conclusions are as follows: (1) Various types of pores were observed in the P2h8 sandstones, including residual intergranular pores, grain dissolution pores and intercrystalline pores, and small micro-cracks. The size ranges of these pore types significantly differ. (2) The total PSD was acquired by integrating the HPMI and CRMI methods, which yielded a

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range from 3.7 nm to 600 µm in radius. The multimodal PSD was characterized by two broad peaks. The right peak, with radii between 50 and 600 µm, indicated a pore body and was associated with residual intergranular pores and partial-dissolution pores in grains. The left peak, which corresponded to throats, showed notable fluctuations and ranged from 3.7 nm to 50 µm; pores within this size range were mostly associated with dissolution pores and intercrystalline pores. (3) The permeability was mainly controlled by relatively large throats with lower percentages. When the permeability was less than 1.0 mD, it was mainly dominated by nanopores and micropores; under the opposite conditions, this factor was dominated by micropores. In addition, nanopores were increasingly important to the reservoir storage capability with decreasing permeability. (4) Unlike the pore throat radii r35, r25 and r10 that were proposed by Winland, Pittman and Rezaee, respectively, the pore throat radius of r30, which was the best pore throat representative for permeability estimation, generated the strongest correlation with the permeability and porosity.

ACKNOWLEDGMENTS This work was financially supported by the National Natural Science Foundation of China (No. 41272157) and the National Science and Technology Major Project of China (Nos. 2016ZX05007-003 and 2016ZX05006-006). We sincerely thank the Research Institute of Exploration and Development of the Changqing Oilfield Company and PetroChina Research Institute of Exploration and Development for providing samples and data access for our experiments. We are also grateful to Dr. Ehsan UI Haq and Dr. Yuqi Zhou for their valuable suggestions.

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APPENDIX A A.1. Winland Model logr35 = 0.732 + 0.588logkair - 0.864logφ………………………………………………..………. (1)

A.2. Pittman Model logk = -1.221 + 1.415logφ + 1.512logr25 ……………………………………….……….…...….. (2)

A.3. Rezaee Model logk = -1.92 + 0.949logφ + 2.18logr10 ………….……………….……………………………..... (3) In all the above empirical formulas, φ and k represent the porosity (%) and air permeability (mD), respectively, and ri represents the pore throat radius (µm), which corresponds to the i-th percentile of mercury saturation on the cumulative mercury curve.

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(38) Simaeys, S. V.; Rendall, B.; Lucia, F. J.; Kerans, C.; Fullmer, S. Facies-independent reservoir characterization of the micropore-dominated Word field (Edwards Formation), Lavaca County, Texas. AAPG Bulletin 2017, 101(1), 73-94. (39) Chehrazi, A.; Rezaee, R. A systematic method for permeability prediction, a petro-facies approach. Journal of Petroleum Science & Engineering 2012, 82-83(2), 1-16. (40) Rezaee, M. R.; Jafaril, A.; Kazemzadeh, E. Relationships between permeability, porosity and pore throat size in carbonate rocks using regression analysis and neural networks. Journal of Geophysics and Engineering 2006, 3, 370-376. (41) Kadkhodaie Ilkhchi, A.; Rezaee, M.; Moallemi, S. A. A fuzzy logic approach for estimation of permeability and rock type from conventional well log data: an example from the Kangan reservoir in the Iran offshore gas field. Journal of Geophysics & Engineering, 3(4), 356-369. (42) Kolodzie, J. S. Analysis of pore throat size and use of the Waxman–Smits equation to determine OOIP in Spindle Field, Colorado. In: Presented at SPE 55th Annual Technical Conference and Exhibition, Dallas, USA. Paper 9382, 21–24, 1980. (43) Pittman, E. D. Relationship of porosity and permeability to various parameters derived from mercury injection-capillary pressure curves for sandstone. AAPG Bulletin 1992, 76(2), 191-198. (44) Hinai, A. A.; Rezaee, R.; Saeedi, A.; Lenormand, R. Permeability prediction from mercury injection capillary pressure: an example from the Perth Basin, Western Australia. Appea Journal 2013, 53, 31-35. (45) Hanson, A. D.; Ritts, B. D.; Mechael Moldowan, J. Organic geochemistry of oil and source rock strata of the Ordos Basin, north-central China. AAPG Bulletin 2007, 91(9), 1273-1293. (46) Yang, H.; Fu, J. H.; Wei, X. S.; Liu, X. S. Sulige field in the Ordos Basin: Geological setting, field discovery and tight gas reservoirs. Marine and Petroleum Geology 2008, 25, 387-400. (47) Guo, H. J.; Jia, W. L.; Peng, P. A.; Lei, Y. H.; Luo, X. R.; et al. The composition and its impact on the methane sorption of lacustrine shales from the Upper Triassic Yanchang Formation, Ordos Basin, China. Marine and Petroleum Geology 2014, 57, 509-520. (48) Ding, X. Q.; Yang, P.; Han, M. M.; Chen, Y.; Zhang, S. Y.; et al. Characteristics of gas accumulation in a less efficient tight-gas reservoir, He 8 interval, Sulige gas field, Ordos Basin, China. Russian

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Geology & Geophysics 2016, 57(7), 1064-1077. (49) Jiang, F.; Chen, D.; Chen, J.; Li, Q.; Liu, Y.; Shao, X.; Hu, T.; Dai, J. Fractal analysis of shale pore structure of continental shale gas reservoir in the ordos basin, nw china. Energy & Fuels 2016, 30(6), 4676-4689. (50) Zhao, W. Z.; Wang, Z. C.; Zhu, Y. X.; Wang, Z. Y.; Wang, P. Y.; Liu, X. S. Forming mechanism of low-efficiency gas reservoir in Sulige gas field of Ordos basin. Acta Petrolei Sinica 2005, 26(5), 5-9. (51) Zhao, W. Z.; Bian, C. S.; Xu, Z. H. Similarities and differences between natural gas accumulations in Sulige gas field in Ordos basin and Xujiahe gas field in Central Sichuan Basin. Petroleum Exploration & Development 2013, 40(4), 429-438. (52) Yang, R. C.; Fan, A. P.; Loon, A. J. V.; Han, Z. Z.; Wang, X. P. Depositional and diagenetic controls on sandstone reservoirs with low porosity and low permeability in the Eastern Sulige gas field, China. Acta Geological Sinica (English Edition) 2014, 88(5), 1513-1534. (53) Liu, R. E.; Wu, H.; Wei, X. S.; Xiao, H. P.; Zhang, C. L. Anomaly distribution and genesis of feldspar in the 8th Member sandstone reservoir of Shihezi formation, Permian, Ordos basin. Journal of China University of Mining & Technology 2017, 46(1), 70-79. (54) Yang, R. C.; Fan, A. P.; Han, Z. Z.; Wang, X. P. Diagenesis and porosity evolution of sandstone reservoirs in the east II part of sulige gas field, Ordos Basin. International Journal of Mining Science and Technology 2012, 22(03), 311-316. (55) Stanton, P. T.; Wilson, M. D. Chapter 14. measurement of independent variables - composition. Short Course Notes 1994, 277-291. (56) Stroker, T. M.; Harris, N. B.; Elliott, W. C.; Wampler, J. M. Diagenesis of a tight gas sand reservoir: Upper Cretaceous Mesaverde Group, Piceance Basin, Colorado. Marine & Petroleum Geology 2013, 40(1), 48-68. (57) Washburn, E. W. The dynamics of capillary flow. Physical Review 1921, 17(3), 273-283. (58) Byrnes, A.; Cluff, R.; Webb, J.; Victorine, J.; Stalder, K.; Osburn, D.; et al. Analysis of critical permeability, capillary pressure and electrical properties for Mesaverde tight gas sandstones from western U.S. basins. Technical Report Submitted to U.S. Department of Energy, U.S. DOE Contract Number: DE-FC26-05NT42660, 2008.

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(59) Gaulier, C. Studying vugular rocks by-constant-rate mercury injection. New Orleans: SPE3612, Presentation at the 46th Annual Fall Meeting of the Society of Petroleum Engineers of AIME, October 3-6, 1971. (60) Iassonov, P.; Gebrenegus, T.; Tuller, M. Segmentation of X-ray computed tomography images of porous materials: a crucial step for characterization and quantitative analysis of pore structures. Water Resources Research 2009, 45(W09415), 706-715. (61) Shearing, P. R.; Bradley, R. S.; Gelb, J.; Lee, S. N.; Atkinson, A.; Withers, P. J.; Brandon, N. P. Using synchrotron X-ray nano-CT to characterize SOFC electrode microstructures in three-dimensions at operating temperature. Electrochemical & Solid State Letters 2011, 14(10), B117-B120. (62) Krakowska, P.; Dohnalik, M.; Jarzyna, J.; Wawrzyniak-Guz, K. Computed X-ray microtomography as the useful tool in petrophysics: a case study of tight carbonates Modryn formation from poland. Journal of Natural Gas Science & Engineering 2016, 31, 67-75. (63) Jiang, F. J.; Chen, J.; Xu, Z. Y.; Wang, Z. F.; Hu, T.; Chen, D.; Li, Q. L.; Li, Y. X. Organic matter pore characterization in lacustrine shales with variable maturity using nanometer-scale resolution X-ray computed tomography. Energy & Fuels 2017, 31, 2669-2680. (64) Ketcham, R. A. Computational methods for quantitative analysis of three dimensional features in geologic specimens. Geosphere 2005, 1(1), 32-41. (65) Loucks, R. G.; Reed, R. M.; Ruppel, S. C.; Hammes, U. Spectrum of pore types and networks in mudrocks and a descriptive classification for matrix-related mudrock pores. AAPG Bulletin 2012, 96(6), 1071-1098. (66) Milliken, K. L.; Reed, R. M. Multiple causes of diagenetic fabric anisotropy in weakly consolidated mud, Nankai accretionary prism, IODP Expedition 316. Journal of Structural Geology 2010, 32(12), 1887-1898. (67) Yao, Y. B.; Liu, D. M.; Che, Y.; Tang, D. Z.; Tang, S. H.; Huang, W. H. Petrophysical characterization of coals by low-field nuclear magnetic resonance (NMR). Fuel 2010, 89, 1371-1380. (68) Gane, Patrick A. C.; Ridgway, Cathy J.; Lehtinen, E.; Valiullin, R.; Furó, I.; Schoelkopf, J.; et al.

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Comparison of NMR cryoporometry, mercury intrusion porosimetry, and DSC thermoporosimetry in characterizing pore size distributions of compressed finely ground calcium carbonate structures. Industrial & Engineering Chemistry Research 2004, 43(24), 7920-7927. (69) Xiao, D. S.; Lu, S. F.; Lu, Z. Y.; Huang, W. B.; Gu, M. W. Combinig nuclear magnetic resonance and rate-controlled porosimetry to probe the pore-throat structure of tight sandstones. Petroleum Exploration & Development 2016, 43(6), 1049-1059. (70) Goel, A.; Tzanakakis, M.; Huang, S. Y.; Ramaswamy, S.; Choi, D.; Ramarao, B. V. Characterization of the three-dimensional structure of paper using X-ray microtomography. Tappi Journal 2001, 84(5), 407-411. (71) Blunt, M. J.; Bijeljic, B.; Dong, H.; Gharbi, O.; Iglauer, S.; Mostaghimi, P.; et al. Pore-scale imaging and modelling. Advances in Water Resources 2013, 51, 197-216. (72) Wildenschild, D.; Sheppard, A. P. X-ray imaging and analysis techniques for quantifying pore-scale structure and processes in subsurface porous medium systems. Advances in Water Resources 2013, 51(1), 217–246. (73) Clarkson, C. R.; Solano, N.; Bustin, R. M.; Bustin, A. M. M.; Chalmers, G. R. L.; He, L.; et al. Pore structure characterization of North American shale gas reservoirs using USANS/SANS, gas adsorption, and mercury intrusion. Fuel 2013, 103, 606-616. (74) Winslow, D. N. The validity of high pressure mercury intrusion porosimetry. Journal of Colloid & Interface Science 1978, 67(1), 42-47. (75) Costa, A. Permeability-porosity relationship: A reexamination of the Kozeny-Carman equation based on a fractal pore-space geometry assumption. Geophysical Research Letters 2006, 33(2), 87-94. (76) Kozeny, J. Uber kapillare Leitung des Wassers im Boden. Sitzungsber. Akad. Wiss. Wien 1927, 136, 271–306. (77) Carman, P. C. Fluid flow through a granular bed. Trans. Inst. Chem. Eng. 1937, 15, 150-156. (78) Zhao, H. W.; Ning, Z. F.; Zhao, T. Y.; Zhang, R.; Wang, Q. Effects of mineralogy on petrophysical properties and permeability estimation of the Upper Triassic Yanchang tight oil sandstones in Ordos basin, Northern China. Fuel 2016, 186, 328-338.

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Table 1. Basic petrophysical parameters of the nine sandstone samples from the study area Depth

Detrital component /%

/m

Quartz

Ss16

3342.98

Ss59

3509.92

Ss61

Porosity

Permeability

Feldspar Rock fragments /mm

/%

/mD

82.7

2.8

14.5

0.40-1.64

10.6

0.631

79.4

3.1

17.5

0.40-0.80

10.9

0.405

3611.39

90.0

2.6

7.4

1.00-1.70

15.1

5.651

Ss100

3390.53

89.6

2.7

7.7

0.64-0.96

10.6

1.739

Ss120

3639.15

82.8

1.6

15.6

0.30-0.75

9.5

0.960

Ss315

3750.65

82.0

1.9

16.1

0.35-1.20

7.2

0.321

Sg52

3269.32

85.4

1.5

13.1

0.40-1.15

8.4

0.923

Sn247

3027.30

87.5

1.3

11.2

1.00-2.00

7.6

2.974

Sn324

3859.30

81.3

1.5

17.2

0.45-1.65

6.8

0.543

Sample

Grain size

Table 2. Pore-structure characteristic parameters of the nine samples Sample

HPMI test data a

Pte /MPa Sm /%

CRMI test data Sr /%

b

Pte /MPa Sm /%

Spb /%

St /%

η

Ss16

0.46

90.82

64.72

0.22

38.00

10.29

27.71

151.74

Ss59

0.72

93.69

47.13

0.13

58.88

12.98

45.90

208.17

Ss61

0.29

88.71

50.19

0.10

71.43

24.74

46.69

76.43

Ss100

0.29

85.73

57.76

0.23

73.91

41.05

32.87

100.85

Ss120

0.74

86.07

69.08

0.28

85.88

59.51

26.37

146.92

Ss315

1.14

81.83

57.32

0.53

67.48

32.63

34.85

180.71

Sg52

0.73

85.63

70.28

0.41

32.55

6.72

25.83

121.19

Sn247

0.18

87.09

56.09

0.09

37.64

13.86

23.78

52.96

Sn324 0.45 82.60 62.34 0.28 54.87 19.01 35.87 184.03 For HPMI: aPte = threshold entry pressure, Sm = maximum mercury saturation, and Sr = residual mercury saturation. For CRMI: bPte = threshold entry pressure, Sm = total mercury injection saturation, St = mercury injection saturation of the throat, Spb = mercury injection saturation of the pore body, and η = pore body to throat ratio.

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Table 3. Controls of the pore throat size on the physical properties of the tight reservoirs Sample

Throat peak cumulative mercury injection saturation /% cumulative permeability contribution /% radius /µm

r < 0.5 µm

r > 0.5 µm

r < 0.5 µm

r > 0.5 µm

Ss16

0.52

70.52

20.3

26.1

73.9

Ss59

0.44

82.69

11.0

41.5

58.5

Ss61

1.73

42.21

46.5

1.2

98.8

Ss100

1.40

53.73

32.0

2.7

97.3

Ss120

0.47

63.67

22.4

32.0

68.0

Ss315

0.26

61.83

20.0

27.0

73.0

Sg52

0.42

63.63

22.0

36.0

64.0

Sn247

1.60

53.49

33.6

1.8

98.2

Sn324

0.60

67.50

15.1

29.0

71.0

Table 4. Empirical permeability evaluation equations from the regression analysis Liner regression equations

a (95% confidence bounds)

b (95% confidence bounds)

c (95% confidence bounds)

R2

log k = a + b log r10 + c log φ

-0.396 (-1.625, 0.833)

1.250 (0.784, 1.716)

0.486 (-0.763, 1.734)

0.9096


-0.288 (-1.421, 0.845)

1.233 (0.819, 1.646)

0.444 (-0.697, 1.584)

0.9251


-0.167 (-1.134, 0.801)

1.258 (0.908, 1.608)

0.408 (-0.555, 1.371)

0.9468


0.182 (-0.832, 1.195)

1.337 (0.967, 1.707)

0.158 (-0.833, 1.148)

0.9472


0.321 (-0.649, 1.291)

1.478 (1.096, 1.860)

0.164 (-0.763, 1.092)

0.9535


0.466 (-0.988, 1.920)

1.567 (0.960, 2.175)

0.214 (-1.129, 1.556)

0.9031


0.463 (-1.620, 2.547)

1.486 (0.621, 2.351)

0.310 (-1.566, 2.186)

0.8126


0.077 (-2.880, 3.033)

1.136 (-0.003, 2.274)

0.642 (-1.979, 3.262)

0.6289


-0.224 (-3.763, 3.314)

0.959 (-0.448, 2.366)

0.953 (-2.069, 3.974)

0.4947


-0.753 (-4.826, 3.320)

0.621 (-0.968, 2.210)

1.330 (-2.032, 4.693)

0.3582


-1.534 (-5.702, 2.635)

0.183 (-1.383, 1.749)

1.780 (-1.638, 5.197)

0.2704

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Figure 1. Regional tectonic unit divisions and location of the sampling wells in the Ordos Basin.

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Figure 2. Generalized Upper Paleozoic stratigraphy of the study area, which shows the major natural gas combinations (modified after Zhao et al., 2014).6

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Energy & Fuels

Figure 3. Schematic diagrams of the pore body and throat distributions as determined by CRMI.

Figure 4. Schematic diagrams of the pore body and throat distributions as determined by micro-CT scanning (modified after Jiang et al., 2017)63: (a) pore system that is filled with spherical clusters, (b) pore body – throat – pore body set.

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Figure 5. Photomicrographs that display the pore types of the P2h8 tight sandstones: (a) quartz secondary enlargement and residual intergranular pores (sample Sg52), PPL; (b) typical triangular residual intergranular pore (sample Sg52), SEM; (c) residual intergranular pore that is filled with illite and authigenic quartz (sample Ss100), SEM; (d) authigenic quartz and chlorite-filled residual intergranular pore (sample Ss120), SEM; (e) intraparticle dissolution pores (sample Ss315), PPL; (f) dissolution pores in the feldspar grain along cleavage planes (sample Ss315), SEM; (g) intraparticle dissolution pores (sample Ss16), PPL; (h) dissolution pores in a quartz grain (sample Sn324), FE-SEM; (i) intercrystalline pores within biotite (sample Ss59), FE-SEM; (j) intercrystalline pores within lamellar chlorite aggregates (sample Ss59), FE-SEM; (k) intercrystalline pores between aggregates of pseudo-hexagonal platy kaolinite (sample Ss59), FE-SEM; (l) intercrystalline pores between aggregates of mixed illite/smectite layers (sample Sn247), FE-SEM. PPL = plane polarized light; F = feldspar; Q = quartz; Qo = quartz overgrowth; Ca = calcite; I/S = mixed illite-smectite layer; I = illite; K = kaolinite; Ch = chlorite; RIP = residual intergranular pore; DP = dissolution pore; IP = intercrystalline pore.

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1000 Solid Points represent injection curve Hollow Points represent ejection curve

Capillary pressure /MPa

100

10

1 Ss61 Type Ⅰ

Ss100

0.1

Sn247 Ss120 Ss16

0.01

Type Ⅱ

Ss59 Sn324 Sg52 Ss315

0.001 100

90

80

70

60

50

40

30

20

10

0

Mercury saturation /%

Figure 6. HPMI-derived capillary pressure curves of the nine selected samples.

30

30

a

b

25

Ss61

20

Ss100

Ss16

25

Hg saturation /%

Ss59 Hg saturation /%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Energy & Fuels

Sn247

15

10

5

0 0.001

Ss120

20

Ss315 15 Sg52 Sn324

10

5

0.01

0.1

1

10

100

0 0.001

1000

0.01

0.1

Pore size distribution /µm

1

10

Pore size distribution /µm

Figure 7. Pore size distribution from the HPMI experiment.

41


100

1000

Energy & Fuels

10

10

Sample Ss59

b Capillary pressure / MPa

Sample Ss100

1

1

CRMI total

CRMI total

CRMI pore body

CRMI pore body CRMI throat

CRMI throat

0.1 100

90

80

70

Capillary pressure / MPa

a

60

50

40

30

20

10

0.1

0

100

90

80

70

Hg saturation / %

60

50

40

30

20

10

0

Hg saturation / %

Figure 8. CRMI-derived capillary pressure curves: (a) sample Ss59 and (b) sample Ss100.

25

25

a

Ss16

20

Ss59

Ss59

Ss61

15

Frequency /%

Frequency /%

b

Ss16

20

Ss100 Ss120 Ss315

10

Sg52

Ss61

15

Ss100 Ss120 Ss315

10

Sg52 Sn247

Sn247

5

5

Sn324

Sn324

0

0 0

100

200 300 Pore body radius /µm

400

0.1

500

25

1 Throat radius /µm

10

30

c

d

Ss16

20

25

Ss16 Ss59 Ss61 Ss100 Ss120 Ss315 Sg52 Sn247 Sn324

Ss59 Ss61

Hg saturation /%

Frequency /%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 42 of 51

Ss100

15

Ss120 Ss315

10

Sg52 Sn247

20 15 10

Sn324

5

5

0

0

10

100

0.1

1000

1

10

100

Pore body-throat radius /µm

Pore body to throat ratio

Figure 9. Parameter-distribution characteristics as revealed by CRMI.

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1000

Page 43 of 51

20

a Porosity /%

16 12 8 4 0 0

50

100 150 200 Pore body to throat ratio

250

10

b Permeability /mD

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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1 y = 10098x-1.905 R² = 0.8256

0.1 0

50

100 150 200 Pore body to throat ratio

250

Figure 10. Correlations between the pore body to throat ratio and the (a) porosity and (b) permeability.

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Figure 11. Sample Ss100 (3390.53 m, φ: 10.6%, K: 1.739 mD): (a) gray-scale micro-CT image cross-section, where the white square was reconstructed for 3-D visualization; (b) 3-D visualization of the ball-and-stick model of the pore-throat network; (c) 3-D visualization of the pore network and its connectivity; and (d) 3-D visualization of the throat network of the sub-volumes.

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Figure 12. Sample Ss59 (3509.92 m, φ: 10.9%, K: 0.405 mD): (a) gray-scale micro-CT image cross-section, where the white square was reconstructed for 3-D visualization; (b) 3-D visualization of the ball-and-stick model of the pore-throat network; (c) 3-D visualization of the pore network and its connectivity; and (d) 3-D visualization of the throat network of the sub-volumes.

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Figure 13. (a) Pore body size distribution from the extracted 3-D images compared to the CRMI pore size distribution; (b) throat size distribution from the extracted 3-D images compared to the CRMI and HPMI throat size distributions.

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1000

a

1000

b

Sample Ss59

Sample Ss100

100

1

HPMI injection

0.1

HPMI ejection

100


10

10

1 HPMI injection

0.1

HPMI ejection

CRMI total

CRMI total

0.01

0.01

CRMI pore body

CRMI pore body

CRMI throat

CRMI throat

0.001 100

90

80

70

60

50

40

30

20

10

0.001

0

100

90

Hg saturation / %

80

70

60

50

40

30

20

10

0

Hg saturation / %

30

c

Left peak

Right peak

Ss16 Ss59

25

HPMI Hg saturation /%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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HPMI and CRMI

CRMI

Ss61

20 Ss100 15

Ss120 Ss315

10

Sg52 5

0 0.001

Sn247 Sn324 0.01

0.1

1 10 Pore sizes distribution (PSD) /µm

100

1000

Figure 14. Comparison of the HPMI and CRMI that were conducted on (a) sample Ss59 and (b) sample Ss100, and (c) the overall connected pore size distribution when merging the HPMI and CRMI analyses.

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100

100

Incremental Hg injection saturation

Cumulative permeability contribution

70

Storage capability 60

Percolation potential 50 40

Peak value

30 20 10

90 80

Incremental permeability contribution Cumulative permeability contribution

70 60

Storage capability Percolation potential

50 40

Main peak

30 20 10 0

a

Pore throat radius (µm)

63.290

180.548

100.513

25.283 40.389

16.147

6.139

10.249

3.924

1.601 2.465

0.996

0.647

0.404

0.252

0.100 0.161

0.064

0.040

0.025

0.016

0.006 0.010

0.004

167.811

62.850 100.584

25.300 40.169

9.947 15.809

3.955 6.141

1.023 1.615 2.658

0.407 0.632

0.164 0.253

0.062 0.100

0.025 0.041

0.010 0.016

0.004 0.006

0

b


100 Incremental Hg injection saturation

90

Cumulative Hg injection saturation

80 Incremental permeability contribution Cumulative permeability contribution

70 60

Percolation potential 50

Storage capability 40

Peak value

32%

30 20 10

100 90

Mercury saturation / Contribution rate (%)

97.3%

80

Storage capability

Percolation potential 70 60 50 40


Peak value

30

Cumulative Hg injection saturation Incremental permeability contribution

20

Cumulative permeability contribution

10



40.414 63.368

100.650

25.274

10.254 16.144

4.017 6.140

1.605 2.503

0.647 0.994

0.252 0.405

0.162

0.064 0.100

0.025 0.040

0.010 0.016

0.004

c

0.004 0.006

0

179.703

63.315

100.522

25.279 40.423

16.149

10.258

4.071 6.140

2.524

1.604

0.993

0.403 0.648

0.252

0.162

0.100

0.040 0.064

0.025

0.016

0.010

0


80


Incremental permeability contribution




90


0.004 0.006

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 48 of 51

d

Figure 15. Controls of the PSD on the storage capability and percolation potential of tight gas sandstone reservoirs: (a) sample Ss59, (b) sample Ss120, (c) sample Ss100, (d) sample Sn247.

48


Page 49 of 51

10

Permeability /mD

a

1 y = 0.2879e1.5255x R² = 0.8579

0.1 0

0.5

1 1.5 Throat peak value /µm

2

10

b Permeability /mD

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Energy & Fuels

1 y = 5E+07x-4.302 R² = 0.7629

0.1 30

40

50 60 70 Nanopore content /%

80

90

Figure 16. Plots that display the relationship between the permeability and the (a) throat peak value and (b) nanopore content.

49


Energy & Fuels

Cumulative Hg injection saturation /%

100 90

a

80

Ss315

Ss59

Sn324

Ss16

Sg52

Ss120

Ss100

Sn247

Ss61

70 60 50 40 30 20 10 0 Nanopore

Micropore Pore throat type

100

Cumulative permeability contribution /%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 50 of 51

90

b

80

Ss315

Ss59

Sn324

Ss16

Sg52

Ss120

Ss100

Sn247

Ss61

70 60 50 40 30 20 10 0 Nanopore

Micropore Pore throat type

Figure 17. (a) Cumulative contribution from the PSD to the storage capacity; (b) cumulative contribution from the PSD to the flow capacity.

50


Page 51 of 51

10

Winland (1980) Pittman (1992)

Calculated permeability /mD

Rezaee (2012)

1

0.1

0.01 0.01

0.1

a

1

10

Measured permeability /mD

10

Calculated permeability /mD

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Energy & Fuels

Ss16

Ss59

Ss61

Ss100

Ss120

Ss315

Sg52

Sn247

Sn324

1

0.1

0.01 0.01

b

0.1

1

10

Measured permeability /mD

Figure 18. Cross-plot that shows the relationship between the measured and calculated permeability: (a) calculated permeability from the Winland model (1980), Pittman model (1992) and Rezaee model (2012); (b) calculated permeability from the new equation (4).

51


Insight into the Pore Structure of Tight Gas Sandstones - American

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