Methane Adsorption Energy Variation Affected by Industrial Components in Deep and Thick Coal Reservoirs

Abstract

The relevant literature indicates that coal facies have a significant impact on the pore structure and adsorption properties of deep coal reservoirs. The content of submicroscopic components is used to calculate the parameters of coal facies. Based on traditional coal phase parameters and ash content, the coal phases of the coal samples in the study area were divided. Based on the adsorption potential theory, the differences in methane adsorption energy changes between different coal phases were compared. The results are as follows. The wet herbaceous swamp facies (type A) could be divided into two subtypes by using the ash yield: subtype A-1 (with an ash yield lower than 20% and a gel index (GI) lower than 5), and subtype A-2 (with an ash yield larger than 20% and a GI lower than 5). With the increase in micro-pore volume shown in A-1 samples, cumulative surface free energy increases linearly and the maximum rate of decline decreases linearly. Coal facies have an important effect on adsorption parameters: V_L increases and P_L decreases with higher structural preservation index (TPI). The effect of a low ash yield and different R_o,max on methane adsorption energy parameters is stronger.

1. Introduction

The early-stage high gas production of deep coalbed methane (CBM) is affected by the free gas content [1,2]. However, achieving stable high-yield gas production in the later stages depends crucially on the adsorption gas content [3,4].

Currently, methane adsorption is studied through methane isothermal adsorption experiments [5,6,7,8,9,10]. The methane isotherm adsorption curve satisfies a good Langmuir equation. The Langmuir volume (V_L) and Langmuir pressure (P_L) in this curve have become the most widely used classical adsorption parameters in the field of coalbed methane [11,12,13,14]. The previous literature has extensively discussed the effects of coal composition (moisture, ash, vitrinite content, coal rank and so on) and temperature–pressure on methane adsorption capacity. Adsorption capacity decreases with higher temperature and with lower pressure. Beyond a critical temperature and pressure threshold, adsorption capacity tends to stabilize [7,11,15]. Moreover, excessive moisture and ash yield occupy nano-pore volume space, resulting in a reduction in the methane adsorption potential and adsorption capacity [16,17,18]. Some studies have demonstrated that the specific surface area (SSA) percentage of nano-pores can exceed 90%, with the SSA of nano-pores being the main factor affecting methane adsorption capacity [14,19,20,21,22].

Moreover, kinetic energy and potential energy variations in adsorbent molecules result in exothermic and endothermic heat during the adsorption process [10,23,24]. Therefore, adsorption energy variations in coal can elucidate various adsorption phenomena. Zhang et al. [25] showed that the surface tension parameter decreases with higher temperature, with the decrease being less pronounced during adsorption than desorption, explaining the adsorption process of CO₂ at high temperature. Lu et al. (2019) [26] studied the adsorption characteristics of coal with different structural deformation, and concluded that methane molecules preferentially occupy positions with strong surface adsorption potential, causing a sharp increase in the adsorption capacity and capacity of methane in the early stage of adsorption. Zhang et al. [27] indicated that the variation in gas adsorption energy affects the change in sample adsorption capacity, with energy variation being affected by the micro-pore structure and internal chemical structure of the coal sample. Moreover, Zhang et al. [28] demonstrated that the isosteric heat gradually decreased with increasing methane adsorption, with water molecules having a limited effect on the isosteric heat.

Several studies have been carried out on methane adsorption, and surface free energy (SFE) varies during the adsorption process in coal reservoirs. Within the same coal seam of deep CBM, where the difference in the buried depth of the coal seam is small, resulting in minimal variation in maximum vitrinite reflectance (R_o,max), the effect of R_o,max on nano-pore structure is weaker [29,30].

2. Sample Collection and Experimental Methods

2.1. Experimental Methods

Following a specific procedure for the collection and packaging of the samples, the samples were transported to the laboratory for experimental testing according to the Chinese national standard GB/T 19222-2003 (2003) [31]. Then, a microscopic maceral analysis of 3 × 3 cm² polished samples was carried out, examining 500 points on each sample, in accordance with the Chinese national standard GB/T 6948-1998(1998) [32]. Moreover, an industrial analysis was conducted on the samples, following the Chinese national standard GB/T 212-2001(2001) [33]. The basic information of all samples is presented in

Table 1. Basic information of coal samples.

Well No.	Sample No.	Ro,max (%)	Vitrinite (%)	Exinite (%)	Inertinite (%)	Mad (%)	Aad (%)	Vdaf (%)	FCdaf (%)
Q-1	Q1	2.01	74.81	0	25.13	0.6	31.51	8.97	58.92
	Q2	2.14	88.27	0	11.72	0.56	36.09	9.33	54.02
M	M1	1.96	59.26	2.36	38.38	0.48	32.83	15.57	51.12
	M2	1.9	47.22	32.29	20.49	0.65	22.84	12.75	63.76
	M3	1.88	50.52	17.53	31.96	0.98	12.69	12.16	74.17
	M4	1.89	52.86	2.02	45.12	0.65	12.23	11.95	75.17
	M5	1.94	58.11	1.69	40.2	0.79	21.46	11.81	65.94
	M6	1.89	33.45	16.72	49.83	1.09	11.04	11.54	76.33
	M7	1.89	59.72	5.56	34.72	0.79	19.43	11.96	67.82
	M8	1.87	41.45	17.09	41.45	0.72	17.63	13.13	68.52
J	J1	1.95	59.44	8.74	31.82	0.65	28.86	12.21	58.28
	J2	1.8	47.79	20.35	31.86	0.72	55.99	/	/
	J3	1.93	28.62	0.67	70.71	0.73	26.28	12.7	60.29
	J4	1.86	48.58	14.18	37.23	0.68	27.85	12.53	58.94
Q-2	Q3	1.99	97.71	2.29	0	0.88	10.39	8.21	80.52
	Q4	2.02	91.79	1.93	6.28	0.74	15.45	8.71	75.1
	Q5	1.97	86.03	2.79	11.17	0.35	38.57	21.59	39.49
	Q6	1.89	47.77	1.62	50.61	0.76	8.16	8.03	83.05
B	B1	2.09	86.34	0.00	13.73	0.63	20.04	9.14	70.19
	B2	2.06	62.57	0.00	37.37	0.45	38.89	9.68	50.98
	B3	2.25	96.93	0.00	3.19	0.5	51.57	/	/
	B4	2.1	79.21	0.00	20.74	0.3	23.67	12.1	63.93

.

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Low-temperature carbon dioxide adsorption test (LTCO₂ GA)

The test was performed upon grinding 10 g of each sample to a 46–60 mesh size, followed by drying in the oven and a degassing treatment. The interval used between balancing points was 10 s for the BSD-PM1 analysis. The pore volume (PV) and SSA of the micro-pores was calculated using the density functional theory model (DFT) [27,34,35].

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High-temperature and high-pressure isothermal adsorption test (HTHP)

This test was conducted in accordance with the standard ‘Experimental Method for High pressure isothermal adsorption of Coal’ (GB/19560-2008 [36]) [37,38]. The test was performed by grinding 100 g of each sample to a 200 mesh size, followed by drying in the oven and a degassing treatment. The coal sample was placed in a water tank at a controlled temperature of 30 °C for 12 h. Then, a vacuum was introduced to the system. High-purity He (volume fraction 99.99%) was utilized to measure the free space volume in the sample tank, followed by evacuating the He to achieve a vacuum again in the experimental device. According to the pressure–equilibrium–pressure process, high-purity methane (volume fraction of 99.99%) was used as the adsorption substance to increase the gas pressure step by step for adsorption until the maximum experimental pressure of 30 MPa was reached. Gas pressure and adsorption data were collected in real time during the adsorption process [7].

2.2. Calculation of SFE

According to the principle of thermodynamics, adsorption potential is the energy involved in the transfer of adsorbate from the non-adsorbed phase to the adsorbed phase for each unit mass of adsorbate [10,39].

$$\epsilon ={\displaystyle \underset{P_i}{\overset{P_0}{\int }}\frac{RT}{P}}\mathrm{d}P=RT\ln ({\displaystyle \frac{P_0}{P_i}})$$

(1)

where ε is the adsorption potential, J·mol⁻¹; P_i is the equilibrium pressure obtained from the sample adsorption isotherm curve, MPa; R is the gas constant, 8.314 J·(mol·K)⁻¹; T is the experimental temperature, 303 K; and P₀ is the saturated vapor pressure, MPa.

Adsorption space is a characteristic parameter reflecting micro-pore structure, and represents the space occupied by the adsorbed gas in the coal pores.

$$\omega =V{\displaystyle \frac{M}{{\rho }_{\mathrm{ad}}}}$$

(2)

where ω is the adsorption space volume, cm³/g; M is the relative molecular weight of methane, 16 g·mol⁻¹; V is the methane adsorption capacity, mol·g⁻¹; ρ_ad is the adsorption phase density, g/cm³.

The SFE is the increment of the system free energy when the unit area of the adsorbent is increased at a constant temperature and constant pressure [29,40,41,42].

$$\mathrm{\Delta }\gamma ={\displaystyle \frac{V_{\mathrm{L}}RT}{V_{0}S}}\ln (1+{\displaystyle \frac{P}{P_{\mathrm{L}}}})$$

(3)

$$\mathrm{\Delta }{\gamma }_P={\displaystyle \frac{V_{\mathrm{L}}RT{P}_L}{V_{0}S(P_L+P)}}$$

(4)

where V_L is the Langmuir volume, reflecting the maximum adsorption capacity of coal, cm³/g; P_L is the Langmuir pressure, which refers to the corresponding pressure when methane adsorption reaches half of the Langmuir volume (MPa); P is the equilibrium pressure, MPa; and ∆γ is the drop value of the SFE, indicating the difference between the SFE in the non-adsorbed gas state and the SFE after the gas is adsorbed, J/m².

3. Results and Discussion

3.1. Coal Quality and Coal Facies Classification

The microscopic observations (Figure 1) indicate that the organic macerals of coal seam 8 are mainly composed of vitrinite, followed by inertinite, whereas the exinite content is the lowest. Vitrinite consists mainly of matrix vitrinite, followed by homogeneous vitrinite (Figure 1a–d,i–l). The inertinite is mainly composed of fusinite and semi-fusinite (Figure 1). The exinite mainly comprises spore powder and resin body. The homogeneous vitrinite content from wells Q32 and M172 is higher than that of wells Q35 and B 15, but the former has a lower content of matrix vitrinite. A high content of matrix vitrinite is beneficial for hydrocarbon generation, indicating that coal samples from wells Q35 and B 15 have high hydrocarbon generation potential. Moreover, the coal samples from wells Q35 and B15 have developed micro-fractures, which is consistent with their semi-bright coal nature (Figure 1).

Based on these results [6,7,8,9,10], TPI, GI, and GWI are independent coal facies parameters, and these were selected to identify coal facies [43]. Based on TPI~GI, three coal facies were determined, including wet forest swamp facies (TPI > 1 and 1 < GI < 5), wet herbaceous swamp facies (TPI < 1 and 1 < GI < 5), and cover grass swamp facies (TPI < 1 and GI > 5). The wet herbaceous swamp facies (type A) and the cover grass swamp facies (type B) are studied in this paper. As illustrated in Figure 2a, Aad~GI is used to distinguish coal facies. Figure 2b shows that the wet herbaceous swamp facies (type A) can be divided into two subtypes by using the ash yield: subtype A-1 (with an ash yield lower than 20% and a GI lower than 5), and subtype A-2 (with an ash yield larger than 20% and a GI lower than 5).

3.2. Effect of Coal Facies and Coal Quality on Methane Adsorption Ability

Figure 3 shows that the isothermal adsorption curves of all samples satisfy the Langmuir equation. The Langmuir volume (V_L) varies between 12 and 24 cm³.g⁻¹, and the Langmuir pressure (P_L) varies from 2.5 to 4.0 MPa (Figure 3a,b). There is no clear variation in V_L and P_L for samples with different coal ranks. There is a relatively weak linear relationship between V_L, P_L, and R_o,max, because the variation in R_o,max is very small among samples, and P_L is mainly related to meso-pores [44]. Figure 3c shows that the V_L of type A-1 is larger than that of type A-2 and type B. The ash yield of types A-2 and B is higher than that of type A-1, and a high ash yield clogs the micro-pore space, resulting in a lower PV and SSA. Figure 3d illustrates that the P_L of type B is larger than that of type A-1 and type A-2, which is related to the meso-pore volume and proximate analysis.

The correlation between micro-pore volume and adsorption parameters is strong (Figure 4a). V_L increases linearly and P_L decreases with a higher micro-pore volume, with V_L showing a stronger correlation with micro-pore volume than P_L. The PV of 0.3~0.6 nm and 0.6~0.8 nm is only linearly correlated with V_L, and the correlation between the PV of 0.3~0.8 nm with P_L is weak (Figure 4b,c). Differing from Figure 4b,c, V_L increases linearly and P_L decreases with the higher volume of 0.8~1.5 nm pores (Figure 4d).

The relationship between coal facies parameters, industrial components, and adsorption parameters is also studied (Figure 5). The correlation between the ash yield and adsorption parameters is weak (Figure 5a), but V_L decreases linearly with as the ash yield of type B samples increases. This can be explained because the variation in R_o,max among type B samples is small compared with the other types of samples (Figure 5d). For samples of the same coal rank, a higher ash yield correlates with a lower adsorption capacity. Figure 5b,c show that coal facies have an important effect on the adsorption parameter, and V_L increases and P_L decreases with higher TPI. A higher TPI indicates that the degradation degree of coal-forming materials is smaller, the preservation degree of organic matter is high in woody plants, and organic primary pores are developed, resulting in a large number of micro-pores. A higher SSA increases the adsorption capacity of coal samples.

The relationship between single- and multi-fractal dimensions and adsorption parameters is also studied (Figure 6). V_L increases gradually with a higher single fractal dimension (D₂). D₂ represents the heterogeneity of 0.8~1.5 nm micro-pore distribution. A higher D₂ value indicates a larger heterogeneity in pore distribution. This implies that the pore structure is more complex, providing a higher micro-pore volume and SSA, resulting in an enhanced adsorption capacity (Figure 6a). There is no obvious relationship between V_L and the multi-fractal dimension (Figure 6b–d). However, P_L increases linearly with higher type A-2 D₋₁₀–D₀ and D₋₁₀–D₁₀, because type A-2 meso-pores are larger than those of type A-1, and a higher meso-pore volume leads to higher P_L. This has also been explained by Zhang et al. [45] (2024).

3.3. Effect of Coal Facies and Coal Quality on SFE Parameters

ε-ω adsorption characteristic curves were obtained using Equations (1)–(4) (Figure 7). The results show that adsorption space decreases as a logarithmic function with higher adsorption potential. As the adsorption pressure increases, the adsorption potential decreases and the adsorption space increases, respectively. The maximum adsorption space of type A-1 is 0.12~0.14 cm³.g⁻¹, followed by type A-2 (0.11~0.12 cm³.g⁻¹), while type B is the lowest (0.09~0.12 cm³.g⁻¹).

Figure 8a shows the SFE as a function of adsorption pressure at a constant temperature. For a pressure below 20 MPa, the SFE increases gradually with higher adsorption pressure. For an adsorption pressure of more than 20 MPa, the SFE tends to be stable with higher adsorption pressure. These results show that adsorption pressure promotes the adsorption of gas on the coal surface, and that free energy on the coal surface leads to a low-energy state. In the initial stage of adsorption, the effect of adsorption pressure on the adsorption ability is obvious. As adsorption proceeds, gas molecules that are already adsorbed on the coal surface will have a repulsive effect on the molecules that still need to be adsorbed, thus reducing the favorable effect of pressure.

Figure 8b shows that the rate at which the SFE decreases also decreases with higher adsorption pressure, which indicates that the adsorption capacity of methane on the coal sample decreases with a longer adsorption time. For an adsorption pressure of less than 20 MPa, the rate at which SFE decreases is the largest, resulting in a rapid decrease in SFE. For an experimental pressure exceeding 20 MPa, the adsorption of methane molecules becomes more difficult, and the rate at which the SFE decreases also gradually decreases, resulting in a slow decrease in SFE. Figure 8c,d show that the maximum SFE of type A-1 and A-2 samples is larger than that of type B samples; the phenomena are consistent with those shown in Figure 3.

The relationship between micro-pore structure and energy parameters was also studied (Figure 9). The correlation between micro-pore volume and energy parameters is weak; there is no significant correlation between the micro-pore structure parameters of type A-2 and type B samples and the adsorption energy parameters (cumulative surface free energy (CSFE) and maximum surface free energy (MSFE) decline rate). However, with the increase in micro-pore volume in type A-1 samples, the cumulative surface free energy increases linearly and the maximum rate of decline decreases linearly (Figure 9). The reason for this is that the type A-1 samples belong to the low-ash wet herbaceous bog phase, and the R_o,max of all samples is 1.88~1.99. The ash yields and R_o,max lead to a large difference in micro-pore volume among the same type of samples, so the correlation between the micro-pore volume and adsorption energy parameters is stronger.

The relationship between coal facies parameters, industrial components, and adsorption energy parameters is studied (Figure 10). Figure 10b,d show that the coal facies and R_o,max have an important effect on the energy parameter; the rate of MSFE decline in type B samples increases with the increase in TPI. Moreover, the cumulative SFE of type A-2 samples increases and the MSFE decline rate of type A-2 samples decreases with the increase in R_o,max, indicating that the degree of coal thermal evolution has an important effect on the adsorption energy parameters. As mentioned above, the R_o,max of type A-2 samples is the most different, resulting in the most obvious control effect of R_o,max on micro-pore structure.

The relationship between the single- and multi-fractal dimension and energy parameter is studied (Figure 11). There is a linear relationship between the single-fractal dimension (D₂), D₀-D₁₀, and adsorption energy parameter of type A-2 samples. This is because the R_o,max of type A-2 samples is the most different, resulting in the most obvious control effect of R_o,max on micro-pore structure. Differing from Figure 11a,b, the cumulative SFE of type A-1 samples decreases and the MSFE decline rate of type A-1 samples increases with the increase in D_-10-D₀ and D_-10-D₁₀, indicating that multi-fractal dimension variation has an important effect on adsorption energy parameters (Figure 11c,d).

Above all, the three coal facies can be divided by using A_ad~GI, including low-ash moist herbaceous bog facies (Type A-1), high-ash moist herbaceous bog facies (Type A-2), and cover grass swamp facies (Type B). R_o,max, ash yield, and fixed carbon content affect micro-pore structure and methane adsorption ability. Coal facies and micro-pore structure affect methane adsorption capacity, and the adsorption energy parameters of different coal facies are obviously different. The effects of low ash yield and different R_o,max values on methane adsorption energy parameters are stronger.

4. Conclusions

With the increase in micro-pore volume seen in the type A-1 sample, the cumulative surface free energy increases linearly and the maximum rate of decline decreases linearly. The reason is that type A-1 samples belong to the low-ash wet herbaceous bog phase. The ash yields and R_o,max lead to a large difference in micro-pore volume among the same types of samples, so the correlation between the micro-pore volume and adsorption energy parameters is stronger.

For the same coal rank, a higher ash yield correlates with a lower adsorption capacity. Coal facies have an important effect on adsorption parameters: V_L increases and P_L decreases with higher TPI. The effect of a low ash yield and different R_o,max values on methane adsorption energy parameters is stronger.