Effects of nano-pore system characteristics on CH₄ adsorption capacity in anthracite

Abstract

This study aims to determine the effects of nanoscale pores system characteristics on CH₄ adsorption capacity in anthracite. A total of 24 coal samples from the southern Sichuan Basin, China, were examined systemically using coal maceral analysis, vitrinite reflectance tests, proximate analysis, ultimate analysis, low-temperature N₂ adsorption–desorption experiments, nuclear magnetic resonance (NMR) analysis, and CH₄ isotherm adsorption experiments. Results show that nano-pores are divided into four types on the basis of pore size ranges: super micropores (<4 nm), micropores (4–10 nm), mesopores (10–100 nm), and macropores (>100 nm). Super micropores, micropores, and mesopores make up the bulk of coal porosity, providing extremely large adsorption space with large internal surface area. This leads us to the conclusion that the threshold of pore diameter between adsorption pores and seepage pores is 100 nm. The “ink bottle” pores have the largest CH₄ adsorption capacity, followed by semi-opened pores, whereas opened pores have the smallest CH₄ adsorption capacity which indicates that anthracite pores with more irregular shapes possess higher CH₄ adsorption capacity. CH₄ adsorption capacity increased with the increase in NMR porosity and the bound water saturation. Moreover, CH₄ adsorption capacity is positively correlated with NMR permeability when NMR permeability is less than 8×10⁻³ md. By contrast, the two factors are negatively correlated when NMR permeability is greater than 8×10⁻³ md.

Keywords

CH₄ adsorption capacity / anthracite / nano-pore structure / NMR physical properties

Introduction

Coalbeds are self-sourcing reservoirs with different characteristics compared with conventional reservoirs (^{Ayers, 2002}). Coal is a complex heterogeneous mixture of organic (maceral) and inorganic (mineral) components and has a very complicated pore structure (^{Gürdal and Yalçın, 2001}). The types of pores and the formation mechanism of coal molecular structure have been studied by many researchers (Gan et al., 1972; ^{Hao, 1987}; ^{Giffin et al., 2013}; ^{Pan et al., 2015a, 2017}). In almost all coal reservoirs, the pore size distribution ranges from microscopic to macroscopic. ^{Sing (1982)} classifies the pores into three categories in the context of physisorption: macropores (>50 nm), mesopores (2‒50 nm), and micropores (<2 nm). The pore structure gives coal a strong ability to adsorb gas because it has large internal surface area which provides numerous active sites for CH₄ adsorption (^{Cai et al., 2013}; ^{Pan et al., 2015a}). ^{Gray (1987)} proposed that approximately 95% of total CH₄ may be adsorbed in the coal matrix. The quantitative evaluation of pore size distribution, pore volume, and pore surface area are very important in understanding CH₄ adsorption capacity and evaluating the gas reserves of coal (^{Cai et al., 2013}; ^{Pan et al., 2015a}). The effects of pore structure on CH₄ adsorption capacity has been studied in four bituminous and subbituminous coals from Northeast China (^{Cai et al., 2013}). The results showed that micropores (2–10 nm) and mesopores (10–100 nm) occur as a part of the coal matrix, providing extremely large internal surface area with a strong affinity to CH₄. Macropores (100–1000 nm) and super macropores (>1000 nm) likely serve mainly as CH₄ transport pathways, and only a small amount of CH₄ is adsorbed in the surface of these pores (^{Cai et al., 2013}).

The characteristics of coal pores have been studied using numerous experimental test methods, such as gas (N₂ or CO₂) adsorption–desorption analyses (^{Radovic et al., 1997}; ^{Yao et al., 2006}; ^{Pan et al., 2015b}), nuclear magnetic resonance (NMR) analysis (^{Yao and Liu, 2006}), small angle X-ray scattering (SAXS) (^{Mitropoulos et al., 1998}; ^{Nakagawa et al., 2000}; ^{Sastry et al., 2000}; ^{Pan et al., 2016}), high resolution transmission electron microscopy, quantitative X-ray CT imaging (^{Karacan and Okandan, 2001};), and small-angle neutron scattering (^{Radlinski et al., 2004}). Among these methods, the N₂ adsorption– desorption experiment has been regarded as the most effective method in studying nano-pore structure (^{Faulon et al., 1994}; ^{Fu et al., 2000}; ^{Diduszko et al., 2000}; ^{Mahnke and Mögel, 2003}; ^{Mahamud et al., 2004}; ^{Rigby, 2005}; Cuerda-Correa et al., 2006). Thus, in this paper, we will study the influence of nano-pore structure by the N₂ adsorption-desorption method in combination with nuclear magnetic resonance (NMR) and CH₄ isotherm adsorption experiments on CH₄ adsorption capacity of samples of Lopingian coal to enhance our understanding of gas storage mechanisms.

Samples and methods

Coal samples

For this study, 24 coal core samples from the Lopingian [Wuchiapingian to Changhsingian (Late Permian), 260–252 Ma] (ICS, 2017) were collected from 11 wells in the coalbed methane (CBM) exploration and production demonstration area in the southern Sichuan basin, China (Fig. 1). To keep samples fresh for extended period of time, all samples were carefully and rapidly covered using cling wrap, and then immediately brought to the laboratory for experiments. The key properties of those coal samples are shown in Table 1. Results of organic petrographic analyses of these samples suggest that the Lopingian coals are typical anthracites with high thermal maturity (the maximum vitrinite reflectance is 2.64%–3.31%). Vitrinite group macerals are the most abundant maceral group, followed by inertinite and inorganic mineral matter.

Experimental methods

To study the influences of coal nano-pore system characteristics on CH₄ adsorption capacity, 24 samples were examined using coal maceral analysis, vitrinite reflectance tests, proximate analysis, ultimate analysis, low-temperature N₂ adsorption- desorption experiments, NMR analysis, and CH₄ isothermal adsorption experiments.

Vitrinite reflectance and maceral composition analysis

Mean maximum vitrinite reflectance (% Ro, max) measurements and maceral group analysis (500 points) were performed in accordance with China National Standards GB/T 6948-2008 and GB/T 5588-2001, respectively. The procedures were conducted on the same polished section of the coal samples using a Leitz MPV-3 photometer microscope.

Proximate and ultimate analyses

Proximate analysis (following China National standards GB/T 212-2008) was performed on the 24 coal core samples. The analysis was conducted on a fully auto-measuring industrial analyzer to determine ash yield, moisture content, volatile material yield, and fixed carbon contents under air dry basis. Ultimate analysis was conducted according to China National standards GB/T 476-2001 using an element analyzer to measure carbon, hydrogen, and nitrogen elements of the 24 samples.

Low-temperature N₂ adsorption–desorption experiment

Low-temperature N₂ adsorption–desorption isotherms were obtained using a Quantachrome QUADRASORB SI Surface Area and Pore Size Analyzer at 77 K following the Chinese petroleum industry standard specification SY/T 6154-1995. Coal samples were crushed into grains of 60–80 mesh size (180–250 mm), dried in an oven at 383 K for 24 h, and degassed under high-vacuum apparatus (<10 mmHg) for 12 h at 383 K. The saturation vapor pressure (p_o) of N₂ at 77 K was determined every 2 h during the experiment using a nitrogen vapor pressure thermometer. The relative pressure (p/p_o) ranged from 0.011 to 0.995, and both adsorption–desorption isotherms were measured to investigate the hysteresis types.

NMR experiment

NMR is used to test movable fluid volume in pores to reflect the effective porosity of the core samples; subsequently, the porosity and irreducible water saturation are utilized to calculate the permeability of the core samples. In the present study, 24 core samples were analyzed using an NMR core analyzer with low magnetic field (RecCore04), in accordance with the Chinese petroleum industry standard specification SY/T6490-2007. In the magnetic field, the NMR signals of a hydrogen nucleus (¹H) present within the pore fluid of a rock can be detected through a relationship between relaxation distribution and relaxation time. This produces the T₂ spectrum. The parameter “T₂” is used to characterize the NMR signal, that is (^{Kenyon, 1992}):

(1)T_2=\frac{V}{\rho S},

where T₂ represents the relaxation time of the fluid in the rock, r is a constant that represents the relaxation intensity of the rock surface, S represents the surface area of the pores, and V represents the volume of the pores.

Thus, physical properties such as porosity, rock permeability, and flow characteristics (including bound water saturation and movable water saturation of the pore fluids) can be determined by the T₂ relaxation time (^{Coates et al., 1999}; ^{Shan et al., 2015}). The area greater than the T₂ cutoff value corresponds to the volume of the movable fluid and that area smaller than the T₂ cutoff value represents the volume of the bound fluid. For this purpose, the saturation of the bound water and the movable fluid can be calculated. The porosity of the samples is calculated directly using the following equation (^{Hodgkins and Howard, 1999}):

(2)\Phi =\left({\displaystyle \frac{A_{\mathrm{t}}}{V_{\mathrm{t}}}}\right)*a+b,

where Ф is the NMR porosity, A_t represents the total NMR signal, V_t is the total volume of the sample, and both a and b are calibration coefficients from test signals of all samples with a linear fit.

According to the bound water saturation, the movable water saturation, and NMR porosity, the NMR permeability can be calculated using the Coates model (^{Coates et al., 1999}):

(3)K={\left({\displaystyle \frac{\Phi }{C}}\right)}^{\mathrm{4}}\times {\left({\displaystyle \frac{S\mathrm{wm}}{S\mathrm{wb}}}\right)}^{\mathrm{2}},

where K is the NMR permeability, Ф is the NMR porosity, S_wm represents the movable water saturation, S_wb is the bound water saturation, and C is the undetermined permeability coefficient.

CH₄ isotherm adsorption experiment

CH₄ isotherm adsorption experiments were measured using the TerraTek Isotherm Measurement System (IS–100) according to the previous research (^{Yao et al., 2008}). All coal samples were prepared for moisture-equilibrium treatment by crushing and sieving to a size range of 0.18–0.25 mm (60–80 mesh) with a weight of 100–125 g (American Society for Testing Material standard). Moisture-equilibrium treatment was processed for at least four days for each sample. After these pretreatments, the coal was placed into the sample cell of the IS-100 for the adsorption isotherm test. The temperature and equilibrium pressure were 28°C and 10 MPa, respectively. Analytical results include the Langmuir volume (V_L) and Langmuir pressure (P_L) under three bases (i.e., air dry, dry ash-free, and equilibrium moisture).

Results

Nano-pore structures

The total specific surface areas (S_BET) calculated from the Brunauer–Emmet–Teller (BET) equation (^{Brunauer et al., 1938}) of the 24 coal core samples by the N₂ adsorption analyses range from 0.611 m²/g to 2.233 m²/g and average to 1.312 m²/g (Table 2). The total pore volumes (V_BJH) from Barrett–Joyner–Halenda (BJH) model (^{Barrett et al., 1951}) of the samples range from 1.751×10^-3 cm³/g to 5.249×10^-3 cm³/g, with an average of 2.641×10^-3 cm³/g (Table 2). The pore diameters of the samples are calculated using the BJH model based on the desorption isothermal. The average pore diameter (d_BJH) ranges from 6.135 nm to 17.842 nm (Table 2). The N₂ adsorption amount varies from 1.0679 cm³/g to 3.3532 cm³/g, with an average of 1.6528 cm³/g for all samples (Table 2). N₂ adsorption shows positive relationships with S_BET and V_BJH (Fig. 2).

A plot of the specific surface area with respect to pore diameter (dS_BJH/dW versus W) is given in Fig. 3(a). The plot of dS_BJH/dW versus W shows that pores in the size range less than 4 nm make up the main contribution to the pore surface area, and the pore concentration decreases with increasing pore size. Figure 3(b) shows the relationship between pore volume and pore-size distribution (dV_BJH/dW versus W), which indicates that pores with a diameter larger than 10 nm give the greater contribution to the pore volume. That is, pores less than 4 nm make up the bulk of coal porosity but pores larger than 10 nm can contribute significantly to the total pore volume. Based on this results and previous studies (^{Hodot, 1966}; ^{Shi and Durucan, 2005}; ^{Yao et al., 2008}, ²⁰⁰⁹), nano-pores are divided into super micropores (<4 nm), micropores (4–10 nm), mesopores (10–100 nm), and macropores (>100 nm) in this study.

Nano-pore shapes

We analyzed 24 coal core samples using the low–temperature N₂ adsorption–desorption experiment. Results show that the N₂ adsorbed amount increases as the pressure increases in all samples. At the pressure range from 0 to approximately 0.9 p/p_o, the adsorption isotherms fit Henry’s law very well, but the latter part of the adsorption curves rise more rapidly (Figs. 4(b), 5(b), and 6(b)), like the “Type II” isotherm (Sing, et al., 1982). The increased N₂ adsorption capacity is caused by capillary condensation in the large coal pores. The relationships between low–temperature N₂ adsorption–desorption isotherms (also known as hysteresis loops) can be used to qualitatively predict the shapes of nanoscale coal pores (^{De Boer, 1958}). The shapes of the hysteresis loops have been grouped into three types: Type D1 (Fig. 4(a)), Type D2 (Fig. 5(a)), and Type D3 (Fig. 6(a)).

Coal pores are also divided into three typical types: Type A, Type B, and Type C (^{Chen and Tang, 2001}). Type A comprises opened pores, including cylinder cavities opened at both ends and parallel plane pores opened on all sides (Fig. 5(c)). Type B comprises semi-closed pores, including cylinder cavities, parallel plane pores, wedge-shaped pores, and tapered holes (Figs. 4(c), 5(c), and 6(c)), all of which are closed at one end. Type C is a special pore with narrow neck and wide body and is often referred to as an “ink bottle” pore (Fig. 6(c)).

The hysteresis loops of 8 coal samples of L10, L11, L12, L13, L14, L15, L16, and L22 belong to D1 type (Figs. 4(a) and 4(b)). The adsorption and desorption curves of this type are nearly all overlapped or overlapped in the low relative pressure part and separated in the high relative pressure part. However, the distance between the adsorption branch and the desorption branch is very short in the separated part, which reflects the poor connectivity of Type B pores (Fig. 4(c)). D2 type is represented by 10 samples of L3, L6, L7, L8, L17, L18, L19, L21, L23, and L24 (Figs. 5(a) and 5(b)). The adsorption and desorption curves are obviously separated in the high relative pressure part, which reflects that coal pores with larger pore size must contain opened pores and may have semi-closed pores. The adsorption branch and desorption branch are overlapped in the low relative pressure part, which reflects that coal pores with smaller pore size are mostly semi-closed pores. That is, these coal pores are composed of Type A and Type B (Fig. 5(c)). The shape of D3 type is special, the hysteresis loops of 6 coal samples of L1, L2, L4, L5, L9, and L20 are the quintessential example of this type (Figs. 6(a) and 6(b)). The desorption branch shows a hysteresis pattern and a plateau at high p/p_o (>0.5), then falls suddenly to overlap the adsorption branch (<0.5 p/p_o). It shows that pore system consisted of Type B pores and Type C pores (Fig. 6(c)).

NMR physical properties

24 coal core samples were tested using NMR experiment in this study. The NMR transverse relaxation time (T₂) spectral response characteristics of all samples are shown in Fig. 7. All the experimental data can be seen in Table 3. The porosities of all samples obtained from the NMR tests range from 2.50% to 8.67% and average to 5.13%. The permeability measurements range from 0.0098×10^-3 md to 20.4739× 10^-3 md with an average of 3.3023×10^-3 md. The bound water saturations range from 74.12% to 98.09% and average to 87.33%. The movable water saturations range from 1.91% to 25.88% and average to 12.67%.

Adsorption capacity from CH₄ isotherms

Results from CH₄ isotherm adsorption experiments under three different bases (air dry basis, dry ash-free basis, and equilibrium moisture basis) of 24 coal samples are shown in Table 4. The Langmuir volume of air dry basis (V_Lad) ranges from 21.71 m³/t to 33.63 m³/t, with the average of 27.37 m³/t. The Langmuir volume of dry ash-free basis (V_Ldaf) is between 31.42 m³/t and 47.00 m³/t, with the average of 38.01 m³/t. The Langmuir volume of equilibrium moisture basis (V_Lem) ranges from 20.73 m³/t to 32.37 m³/t, with an average of 25.96 m³/t. The Langmuir pressure (P_L) values of the three different bases are the same, ranging from 1.79 MPa to 3.13 MPa, with an average of 2.35 MPa. Results demonstrate that anthracite has a larger CH₄ adsorption capacity than subbituminous and bituminous coals (^{Yao et al., 2008}; ^{Cai et al., 2013}).

Discussion

Effects of pore surface and volume on CH₄ adsorption capacity

The effects of pore surface and volume on the CH₄ adsorption capacity of 24 anthracite samples are shown in Fig. 8. The Langmuir volume was calculated on a dry ash-free basis. In general, CH₄ adsorption capacity increased with the increase in pore surface (Fig. 8(a)) and volume (Fig. 8(b)). Coals with higher pore surface area and volume have stronger CH₄ adsorption capability because higher internal surface area and volume provide more adsorption sites and space for CH₄, leading to higher adsorption capacity of coals.

Effects of pore size ranges on CH₄ adsorption capacity

Based on pore size distribution, nano-pores of the studied coals have been divided into 4 types: super micropores (<4 nm), micropores (4–10 nm), mesopores (10–100 nm), and macropores (>100 nm). To better understand the effects of different pore sizes on CH₄ adsorption capacity, we will discuss the contribution of super micropores (<4 nm), micropores (4–10 nm), mesopores (10–100 nm), and macropores (>100 nm) to the total specific surface area and pore volume, because we know that significant positive correlation exists between CH₄ adsorption capacity, pore surface area, and pore volume.

Pore specific surface area (from BJH model) of super micropores, micropores, mesopores, and macropores of the 24 samples are shown in Table 2. The super micropores specific surface area (S_s-mic) ranges from 0.221 to 1.026 m²/g, with an average of 0.677 m²/g. The micropores specific surface area (S_mic) is 0.163‒0.663 m²/g, averaged at 0.389 m²/g. The mesopores specific surface area (S_mes) ranges from 0.084 to 0.363 m²/g, with an average of 0.159 m²/g. The macropores specific surface area (S_mac) averages 0.010 m²/g (from 0.002 to 0.036 m²/g). The contribution of these four types of pores to the total specific surface area can be seen in Table 2 and Fig. 9. Pore volumes (from BJH model) of super micropores, micropores, mesopores, and macropores of the 24 samples are shown in Table 2. The super micropores volume (V_s-mic) ranges from 0.171×10^-3 to 0.773×10^-3 cm³/g, with an average of 0.504×10^-3 cm³/g. The micropores volume (V_mic) ranges from 0.272×10^-3 to 1.068×10^-3 cm³/g, averaged at 0.616×10^-3 cm³/g. The mesopores volume (V_mes) ranges from 0.577×10^-3 to 2.739×10^-3 cm³/g, with an average of 1.136×10^-3 cm³/g. Macropores volume (V_mac) ranges from 0.059 ×10^-3 to 1.225×10^-3 cm³/g, with an average of 0.385×10^-3 cm³/g. The contribution of all nano-pores to the total pores volume can be seen in Table 2 and Fig. 10.

In summary, super micropores make the greatest average contribution to the total pore specific surface area, followed by micropores, together they account for 86.1 percent. Mesopores and macropores contribute very little to the total pore specific surface area (Fig. 9). In addition, mesopores give the greatest average contribution to the total pore volume, reaching 42.0%. The pores volume of super micropores and micropores themselves is much smaller than that of mesopores, however, they still contribute 44.1% of the total pores volume. The average contribution of macropores to the total pores volume is much smaller than that of the other three pore types, only 13.9% of all the contribution (Fig. 10). The results indicate that super micropores, micropores, and mesopores make up the bulk of coal porosity, providing extremely large adsorption space with large internal surface area. We can come to a preliminary conclusion that the threshold of pores diameter between adsorption pores and seepage-pores is 100 nm in the studied coals.

Effects of pore shapes on CH₄ adsorption capacity

From the results in 3.2, we have learned that pore shapes fall into three types (Type A, Type B, and Type C). L3, L6, L7, L8, L17, L18, L19, L21, L23, and L24 samples are mainly composed of both Type A and Type B pores; L10, L11, L12, L13, L14, L15, L16, and L22 samples are mainly composed of Type B pores; L1, L2, L4, L5, L9, and L20 are composed of both Type B and Type C pores. To correlate CH₄ adsorption capacity with nanoscale coal pore shapes, the Langmuir volume data for dry ash-free basis is used. From the V_Ldaf results in Table 4, the calculated average V_Ldaf of L3, L6, L7, L8, L17, L18, L19, L21, L23, and L24 samples is 36.75 cm³/g; the average V_Ldaf of L1, L2, L4, L5, L9, and L20 is 40.33 cm³/g; and the average V_Ldaf of L10, L11, L12, L13, L14, L15, L16, and L22 is 37.79 cm³/g. So there seems to be a significant trend of the V_Ldaf: Type B+ Type C>Type B>Type A+ Type B, corresponding to the prediction that “ink bottle” pores have the largest CH₄ adsorption capacity, followed by semi-opened pores, with the opened pores possessing the least CH₄ adsorption capacity. We can further predict that anthracite pores with more irregular shapes have higher CH₄ adsorption capacity.

Effects of NMR physical properties on CH₄ adsorption capacity

From the experimental data of NMR analyses (Table 3) and Langmuir volume calculated on a dry ash-free basis (Table 4), the relationships among CH₄ adsorption capacity, NMR porosity, NMR permeability, and the bound water saturation are discussed.

A plot of V_Ldaf with respect to NMR porosity is given in Fig. 11(a), showing that a positive correlation exists between CH₄ adsorption capacity and NMR porosity, as demonstrated by the increase in CH₄ adsorption capacity with the increase in NMR porosity. It is generally known that porosity is the ratio of the total pore space to the total sample volume, so there is also a positive relationship between the total pore space. That is, the positive correlation between CH₄ adsorption capacity and NMR porosity can prove that anthracite pores are mainly composed of adsorption pores (super-micropore, micropores, and mesopores).

Figure 11(b) shows the relationship between V_Ldaf and NMR permeability. A positive correlation occurs when NMR permeability is less than approximately 8×10^-3 md, but a negative correlation occurs when it is greater than 8×10^-3 md. This behavior is caused by macropores (seepage-pores), which are mainly permeable pathways for gas and water migration (^{Yao et al., 2009}). When coal NMR permeability is less than 8×10^-3 md, pores contain very few macropores, and mesopores play the major role in increasing coal permeability. The increase in NMR permeability means that the number of mesopores gradually increases, leading to the increase in CH₄ adsorption capacity, because mesopores are one type of adsorption pores. When coal NMR permeability is larger than 8×10^-3 md, it can be predicted that some macropores in coal start to provide pathways for fluid migration, the number of macropores increases with the increasing NMR permeability; in other words, the number of adsorption pores becomes a relative reduction, so the CH₄ adsorption capacity decreases with the increase of NMR permeability.

The relationship between V_Ldaf and the bound water saturation is shown in Fig. 11(c). We can see that there is a positive correlation between CH₄ adsorption capacity and the bound water saturation. That is, CH₄ adsorption capacity increases with the increase in the bound water saturation. This is because pores internal surface-adsorbed water molecules of coals increase with the increase in the bound water saturation, thus providing CH₄ with more adsorption sites.

Conclusions

In this paper, a combined experiment (e.g., coal maceral analysis, vitrinite reflectance tests, proximate analysis, ultimate analysis, low-temperature N₂ adsorption-desorption experiments, NMR analysis, and CH₄ isothermal adsorption experiments) were conducted on 24 anthracite samples to determine the effects of pore surface, pore volume, pore size ranges, pore shapes, and physical properties on CH₄ adsorption capacity. Anthracites with higher pore surface area and volume have higher CH₄ adsorption capacities due to the availability of more sites and space for CH₄ adsorption. Nano-pores are divided into super micropores (<4 nm), micropores (4–10 nm), mesopores (10–100 nm), and macropores (>100 nm). Super micropores are the largest category of pores, but mesopores and macropores make more contribution to the total pore volume. Super micropores, micropores, and mesopores make up the bulk of coal porosity, providing extremely large adsorption space with large internal surface area, which leads to the prediction that the threshold of pores diameter between adsorption pores and seepage-pores is 100 nm in the studied anthracites. Coal pores are divided into three types based on pore shape, namely opened pores, semi-closed pores, and “ink bottle” pores. The “ink bottle” pores have the largest CH₄ adsorption capacity, followed by semi-opened pores, and then the opened pores. Therefore, anthracite pores with more irregular shapes have higher CH₄ adsorption capacity. CH₄ adsorption capacity increased with the increase in NMR porosity and the bound water saturation, respectively. CH₄ adsorption capacity is positively correlated with NMR permeability when NMR permeability is less than 8×10^-3 md, and negatively correlated when the permeability is greater than 8×10^-3 md.

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