Inﬂuencing Factors and Mathematical Prediction of Shale Adsorbed Gas Content in the Upper Triassic Yanchang Formation in the Ordos Basin, China

: Evaluating absorbed gas content (AGC) in shales is crucial for accurately characterizing shale gas reservoirs and calculating resource potential. To investigate geological factors inﬂuencing AGC, 15 shale samples collected from the Yanchang Formation underwent related experiments. Then geochemistry features, mineral compositions, pore structure parameters and external factors were analyzed. The actual AGC was calculated using the Langmuir equation. Single geological factors acting on the AGC were discussed by the single-factor correlation analysis. Finally, four main inﬂuence factors (total organic carbon, S 1 , quartz content and formation temperature) were selected out from the 12 inﬂuence factors to establish the mathematical prediction model through the multi-factor regression statistical analysis method using SPSS software. The model was veriﬁed as being reliable with R 2 as high as 0.8046 and relative error less than ± 20%. Comparisons show that both the CH 4 isothermal adsorption experimental method and the multi-factor regression analysis method have their own applicability and disadvantages, and they can complement each other in evaluating AGC in shales. Synthetic evaluation of AGC indicates that the Yanchang shale has an overall moderate AGC occupying about 58% of the total, which is helpful to extend shale gas production time of the Yanchang reservoir. Though under the present conditions, economic beneﬁts of the continental shale gas are not obvious, the shale resource potential of Yanchang formation can’t be ignored.


Introduction
Unconventional oil and gas development from shales has revolutionized the world energy landscape, especially shale gas [1,2]. Marine shale gas in China, represented by Longmaxi formation shale in the Sichuan Basin, has achieved considerable production, showing great shale gas potential [3]. In contrast, most of the continental shale gas in China is still in the resource evaluation stage due to its low maturity and lower gas show in exploration wells [3][4][5]. Accurate acquisition of shale gas content is of great significance for analyzing gas accumulation and assessing gas resources [6][7][8][9][10][11]. remove moisture and impurities on the surface of the sample, and then conducted the CH 4 isothermal adsorption experiments with the highest temperature 120 • C and the highest pressure 18 MPa. Figure 1a presented the OM abundance and OM maturity. It showed that TOC content was in the range of 1.37-7.44 wt % (average 5.00 wt %), R o values ranged from 0.71% to 1.06% (average 0.93%). Results indicated that these shale samples were abundant in OM and they were in the mature stage, which means large amount of oil and gas was generated, the generated gas and residual oil can coexist in shale pores. According to D. W. Van Krevelen chart, OM types were divided [59], shown in Figure 1b, the value of H/C was 0.82-1.14, O/C was 0.11-0.17, so these shale samples could be classified into type II. Due to the thermal evolution, the current range of H/C has reduced and O/C in kerogen has increased compared to the original kerogen thus the real kerogen type of organic matter is prone to sapropelic, indicating the Yanchang shales are better hydrocarbon source rocks. The results are consistent with previous studies [54].

Organic Geochemistry Chracteristics
Minerals 2019, 9, x FOR PEER REVIEW 4 of 25 Figure 1a presented the OM abundance and OM maturity. It showed that TOC content was in the range of 1.37-7.44 wt % (average 5.00 wt %), Ro values ranged from 0.71% to 1.06% (average 0.93%). Results indicated that these shale samples were abundant in OM and they were in the mature stage, which means large amount of oil and gas was generated, the generated gas and residual oil can coexist in shale pores. According to D. W. Van Krevelen chart, OM types were divided [59], shown in Figure 1b, the value of H/C was 0.82-1.14, O/C was 0.11-0.17, so these shale samples could be classified into type II. Due to the thermal evolution, the current range of H/C has reduced and O/C in kerogen has increased compared to the original kerogen thus the real kerogen type of organic matter is prone to sapropelic, indicating the Yanchang shales are better hydrocarbon source rocks. The results are consistent with previous studies [54].

Mineral Compositions
As Figure 2 shows, there was a wide range of mineral constituents in these samples, clay minerals and quartz were two dominated compositions. The average content of the clay mineral was 48.3 wt % (24.5-60.8 wt %), the average content of quartz was 24.5 wt % (7.2-39.6 wt %), indicating that Yanchang shales were clay-rich shales. The third abundant mineral was feldspar (anorthose and orthoclase), followed by carbonates (calcite, dolomite and ferrodolomite). Pyrite was a fine-grained mineral widely distributed in shale gas reservoirs, and it was also a typical mineral for identifying the sedimentary environment for OM enrichment.

Mineral Compositions
As Figure 2 shows, there was a wide range of mineral constituents in these samples, clay minerals and quartz were two dominated compositions. The average content of the clay mineral was 48.3 wt % (24.5-60.8 wt %), the average content of quartz was 24.5 wt % (7.2-39.6 wt %), indicating that Yanchang shales were clay-rich shales. The third abundant mineral was feldspar (anorthose and orthoclase), followed by carbonates (calcite, dolomite and ferrodolomite). Pyrite was a fine-grained mineral widely distributed in shale gas reservoirs, and it was also a typical mineral for identifying the sedimentary environment for OM enrichment.

Pore Types
In the FE-SEM images, the Yanchang shales were very tight, and their pores were mainly micro and nano scales, with diameter ranging from 6.25 nm to 433.2 nm, displaying heterogeneous pore structures. In Figure 3a-c, the OM was associated with clay minerals, the OM pores were irregular but not developed due to the low thermal evolution degree. The intergranular pore was the dominating pore system in the Yanchang shales. They were largely developed between minerals and quartz grains, and they were typically irregular polygons (Figure 3d-f). Pyrite framboid was the most developed pyrite type in the Yanchang shale matrix, and OM were generally filled with the pyrite framboids, thus a lot of organic nanopores were developed in the intergranular organic matter. Intercrystallite pores were identified within incompletely filled pyrite framboids and quartz grains (Figure 3g,h). Fracture pores (widths  nm; lengths about 5 um) were also observed, whose shapes were irregular elongated lines generally existing along grain rims (Figure 3i).

Pore Types
In the FE-SEM images, the Yanchang shales were very tight, and their pores were mainly micro and nano scales, with diameter ranging from 6.25 nm to 433.2 nm, displaying heterogeneous pore structures. In Figure 3a-c, the OM was associated with clay minerals, the OM pores were irregular but not developed due to the low thermal evolution degree. The intergranular pore was the dominating pore system in the Yanchang shales. They were largely developed between minerals and quartz grains, and they were typically irregular polygons (Figure 3d-f). Pyrite framboid was the most developed pyrite type in the Yanchang shale matrix, and OM were generally filled with the pyrite framboids, thus a lot of organic nanopores were developed in the intergranular organic matter. Intercrystallite pores were identified within incompletely filled pyrite framboids and quartz grains (Figure 3g,h). Fracture pores (widths 33-200 nm; lengths about 5 µm) were also observed, whose shapes were irregular elongated lines generally existing along grain rims (Figure 3i).

Pore Structure Parameters
The experimental N 2 adsorption/desorption isotherms of Sample 1 and Sample 7 are illustrated in Figure 4. Based on the IUPAC classification, isotherms of both of the two samples fell in Type IV (isotherms with hysteresis loop) [60], thus indicating the shale samples contained mesopores and macropores [61]. When the value of P/P 0 was less than 0.45, the adsorption branch of isotherms was coincident with the desorption branch, suggesting that small pores were accessible via a single pore throat. As the value of P/P 0 increased, the adsorption and desorption branches gradually separated, leading to the formation of a hysteresis loop at relative pressures of 0.45-1 (Figure 4), which can be attributed to the difference between the adsorption and desorption mechanism corresponding to condensation and evaporation, respectively, indicating that the studied shale samples contained mesopores [61]. (e) intergranular pores between quartz grains and organic matter; (f) intergranular pores and micro fracture in the calcites; (g) intercrystallite pores in the primary pyrite framboids; (h) unpolished with argon ion, intercrystallite pores in the quartz grains; (i) micro fractures in the mineral grains.

Pore Structure Parameters
The experimental N2 adsorption/desorption isotherms of Sample 1 and Sample 7 are illustrated in Figure 4. Based on the IUPAC classification, isotherms of both of the two samples fell in Type IV (isotherms with hysteresis loop) [60], thus indicating the shale samples contained mesopores and macropores [61]. When the value of P/P0 was less than 0.45, the adsorption branch of isotherms was coincident with the desorption branch, suggesting that small pores were accessible via a single pore throat. As the value of P/P0 increased, the adsorption and desorption branches gradually separated, leading to the formation of a hysteresis loop at relative pressures of 0.45-1 (Figure 4), which can be attributed to the difference between the adsorption and desorption mechanism corresponding to condensation and evaporation, respectively, indicating that the studied shale samples contained mesopores [61].  N2 adsorption isotherms can provide information on pore structure parameters, such as the specific surface area (SSA), pore diameter (PD) and pore volume (PV). The equivalent SSA can be calculated by the multi-point BET equation [62], and the results showed that the SSA of these shale samples was in the range of 2.45-6.55 m 2 /g, shown in Figure 5. The large SSA of shales mainly resulted from the high OM content, clay mineral content and fine grain size [19]. Then, the N2 adsorption volume can be used to calculate the values of PV and PD, using the Kelvin equation and BJH model [63]. The PV at a P/P0 value of about 0.98 varying in the range of 0.012-0.027 ml/g in these shale samples, and there was positive correlation between PV and SSA ( Figure 5). N 2 adsorption isotherms can provide information on pore structure parameters, such as the specific surface area (SSA), pore diameter (PD) and pore volume (PV). The equivalent SSA can be calculated by the multi-point BET equation [62], and the results showed that the SSA of these shale Minerals 2019, 9,265 7 of 25 samples was in the range of 2.45-6.55 m 2 /g, shown in Figure 5. The large SSA of shales mainly resulted from the high OM content, clay mineral content and fine grain size [19]. Then, the N 2 adsorption volume can be used to calculate the values of PV and PD, using the Kelvin equation and BJH model [63]. The PV at a P/P 0 value of about 0.98 varying in the range of 0.012-0.027 ml/g in these shale samples, and there was positive correlation between PV and SSA ( Figure 5). N2 adsorption isotherms can provide information on pore structure parameters, such as the specific surface area (SSA), pore diameter (PD) and pore volume (PV). The equivalent SSA can be calculated by the multi-point BET equation [62], and the results showed that the SSA of these shale samples was in the range of 2.45-6.55 m 2 /g, shown in Figure 5. The large SSA of shales mainly resulted from the high OM content, clay mineral content and fine grain size [19]. Then, the N2 adsorption volume can be used to calculate the values of PV and PD, using the Kelvin equation and BJH model [63]. The PV at a P/P0 value of about 0.98 varying in the range of 0.012-0.027 ml/g in these shale samples, and there was positive correlation between PV and SSA ( Figure 5).  Figure 6 shows the relationship between dV/d(logD) and PD for some samples. Generally, there were two peaks in the curves which fell on two intervals, respectively, and the dominant pores were mesopores with diameters ranging from 25.3-29.5 nm, most of the pores were classified as mesopores according to the IUPAC classification [60]. Moreover, the average PD of the studied shale samples was in the range of 15.66-21.74 nm with a mean of 17.78 nm. The larger pore size was to the benefit of free gas transportation, and the lower pore size made for gas adsorption, which agreed with the analysis of N2 adsorption-desorption isotherms from Figure 4.  Figure 6 shows the relationship between dV/d(logD) and PD for some samples. Generally, there were two peaks in the curves which fell on two intervals, respectively, and the dominant pores were mesopores with diameters ranging from 25.3-29.5 nm, most of the pores were classified as mesopores according to the IUPAC classification [60]. Moreover, the average PD of the studied shale samples was in the range of 15.66-21.74 nm with a mean of 17.78 nm. The larger pore size was to the benefit of free gas transportation, and the lower pore size made for gas adsorption, which agreed with the analysis of N 2 adsorption-desorption isotherms from Figure 4.

Methane Adsorption Content
Isothermal adsorption experiment was used to obtain actual AGC, which was an effective method to describe the shale gas adsorption characteristics. In this study, the experimental pressure was not that high, the relation between real adsorption content and pressure were accord with Langmuir adsorption curve, so the Langmuir model was used to calculate the AGC [1,64]. The formula is as follows: where, Va refers to the AGC, m 3 /t; P refers to the formation pressure, MPa; VL refers to Langmuir

Methane Adsorption Content
Isothermal adsorption experiment was used to obtain actual AGC, which was an effective method to describe the shale gas adsorption characteristics. In this study, the experimental pressure was not that high, the relation between real adsorption content and pressure were accord with Langmuir adsorption curve, so the Langmuir model was used to calculate the AGC [1,64]. The formula is as follows: where, V a refers to the AGC, m 3 /t; P refers to the formation pressure, MPa; V L refers to Langmuir volume, cm 3 /g; P L refers to Langmuir pressure, MPa. Table 1 exhibited the CH 4 adsorption isotherm results at 30 • C for the shale samples under various humidity (H) and increasing pressures (P) up to 18 MPa. Then, V L and P L were obtained based on the data of P and measured CH 4 content, according to Equation (1), the formation pressure of shale samples was about 8.6-8.9 MPa, the actual AGC (V a ) of these shale samples were in the range of 0.83-1.60 m 3 /t (average 1.15 m 3 /t), indicating that the overall shale samples have a moderate CH 4 adsorption capacity because of their lower thermal maturity.   Common parameters characterizing OM abundance are TOC, chloroform bitumen "A" and hydrocarbon generation potential (S 1 + S 2 ). As is shown in Figure 1, the Yanchang shales are rich in OM with TOC content larger than 1.0%, and V a has a significantly positive correlation with TOC ( Figure 7). Higher TOC means better shale gas potential, because OM is the material base of shale gas generation, and OM is the most important carrier for shale gas adsorption, enhancing the adsorption capacity of shale. Sander et al. [65] also demonstrated the strong relationship between TOC and shale adsorption capacity by comparing two global shale data sets, however, he also indicated TOC alone cannot account for the differences in AGC.

Geochemical Influence Factors
Organic Matter Abundance Common parameters characterizing OM abundance are TOC, chloroform bitumen "A" and hydrocarbon generation potential (S1 + S2). As is shown in Figure 1, the Yanchang shales are rich in OM with TOC content larger than 1.0%, and Va has a significantly positive correlation with TOC ( Figure 7). Higher TOC means better shale gas potential, because OM is the material base of shale gas generation, and OM is the most important carrier for shale gas adsorption, enhancing the adsorption capacity of shale. Sander et al. [65] also demonstrated the strong relationship between TOC and shale adsorption capacity by comparing two global shale data sets, however, he also indicated TOC alone cannot account for the differences in AGC. Chloroform bitumen "A" is the OM extracted from the rock with chloroform, including saturated hydrocarbon, aromatic hydrocarbon, non-hydrocarbon and asphaltene. HC is the total content of saturated hydrocarbon and aromatic hydrocarbon. Studies show that Va positively correlates with chloroform asphalt "A", the higher the content of chloroform asphalt "A" is, the higher AGC in the shale is ( Figure 8). Chloroform bitumen "A" is the OM extracted from the rock with chloroform, including saturated hydrocarbon, aromatic hydrocarbon, non-hydrocarbon and asphaltene. HC is the total content of saturated hydrocarbon and aromatic hydrocarbon. Studies show that V a positively correlates with chloroform asphalt "A", the higher the content of chloroform asphalt "A" is, the higher AGC in the shale is ( Figure 8). For the main components chloroform bitumen "A", no obvious correlation was found between Va and saturated hydrocarbon content, aromatic hydrocarbon content, as well as HC, indicating that only chloroform bitumen "A" itself affects the AGC, while its components have no obvious influence ( Figure 9). For the main components chloroform bitumen "A", no obvious correlation was found between V a and saturated hydrocarbon content, aromatic hydrocarbon content, as well as HC, indicating that only chloroform bitumen "A" itself affects the AGC, while its components have no obvious influence ( Figure 9). For the main components chloroform bitumen "A", no obvious correlation was found between Va and saturated hydrocarbon content, aromatic hydrocarbon content, as well as HC, indicating that only chloroform bitumen "A" itself affects the AGC, while its components have no obvious influence ( Figure 9). Hydrocarbon generation potential (S1 + S2) refers to the sum of residual hydrocarbon (S1) and pyrolysis hydrocarbon (S2), which are parameters obtained by rock pyrolysis experiment. Figure 10 shows that Va has a significantly positive correlation with all the pyrolysis parameters S1, S2 and S1 + S2. The higher S1 indicates the higher residual hydrocarbon potential of the shale, so the adsorption capacity is larger; the higher S2 indicates more hydrocarbons are produced from kerogen in the process of pyrolysis, which directly increase the AGC in shales; S1 + S2 represents the whole hydrocarbon generating capacity of shale, so the higher S1 + S2 is, the higher AGC in shales is.  Hydrocarbon generation potential (S 1 + S 2 ) refers to the sum of residual hydrocarbon (S 1 ) and pyrolysis hydrocarbon (S 2 ), which are parameters obtained by rock pyrolysis experiment. Figure 10 shows that V a has a significantly positive correlation with all the pyrolysis parameters S 1 , S 2 and S 1 + S 2 . The higher S 1 indicates the higher residual hydrocarbon potential of the shale, so the adsorption capacity is larger; the higher S 2 indicates more hydrocarbons are produced from kerogen in the process of pyrolysis, which directly increase the AGC in shales; S 1 + S 2 represents the whole hydrocarbon generating capacity of shale, so the higher S 1 + S 2 is, the higher AGC in shales is. For the main components chloroform bitumen "A", no obvious correlation was found between Va and saturated hydrocarbon content, aromatic hydrocarbon content, as well as HC, indicating that only chloroform bitumen "A" itself affects the AGC, while its components have no obvious influence ( Figure 9). Hydrocarbon generation potential (S1 + S2) refers to the sum of residual hydrocarbon (S1) and pyrolysis hydrocarbon (S2), which are parameters obtained by rock pyrolysis experiment. Figure 10 shows that Va has a significantly positive correlation with all the pyrolysis parameters S1, S2 and S1 + S2. The higher S1 indicates the higher residual hydrocarbon potential of the shale, so the adsorption capacity is larger; the higher S2 indicates more hydrocarbons are produced from kerogen in the process of pyrolysis, which directly increase the AGC in shales; S1 + S2 represents the whole hydrocarbon generating capacity of shale, so the higher S1 + S2 is, the higher AGC in shales is.

Organic Matter Maturity
As previously concluded, the studied shales are in the mature stage. Figure 11 shows that, V a has no significant correlation with R o . This is mainly because shale in the mature stage it has not yet begun to largely produce shale gas, there is still a little of residual oil in it, which makes some gas dissolved in the residual oil, influencing the adsorption of gas molecules and AGC in shale. The result is consistent with the previous conclusion [66].

Organic Matter Maturity
As previously concluded, the studied shales are in the mature stage. Figure 11 shows that, Va has no significant correlation with Ro. This is mainly because shale in the mature stage it has not yet begun to largely produce shale gas, there is still a little of residual oil in it, which makes some gas dissolved in the residual oil, influencing the adsorption of gas molecules and AGC in shale. The result is consistent with the previous conclusion [66].

Mineral Influence Factors Clay Minerals Content
It's well known that the clay minerals content could affect the AGC [7, 29,67]. In this study, Va and clay mineral content are positively correlated (Figure 12), this is maybe due to the increase of clay minerals increasing the PD and SSA of shale to some extent, so the adsorption capacity and adsorbed gas content is also increased. Gu et al. [29] further described the adsorption characteristics of main components of clay minerals and concluded that the Montmorillonite has the maximum adsorption capacity, followed by Kaolinite and Chlorite, while illite has the least.

Clay Minerals Content
It's well known that the clay minerals content could affect the AGC [7, 29,67]. In this study, V a and clay mineral content are positively correlated (Figure 12), this is maybe due to the increase of clay minerals increasing the PD and SSA of shale to some extent, so the adsorption capacity and adsorbed gas content is also increased. Gu et al. [29] further described the adsorption characteristics of main components of clay minerals and concluded that the Montmorillonite has the maximum adsorption capacity, followed by Kaolinite and Chlorite, while illite has the least.

Organic Matter Maturity
As previously concluded, the studied shales are in the mature stage. Figure 11 shows that, Va has no significant correlation with Ro. This is mainly because shale in the mature stage it has not yet begun to largely produce shale gas, there is still a little of residual oil in it, which makes some gas dissolved in the residual oil, influencing the adsorption of gas molecules and AGC in shale. The result is consistent with the previous conclusion [66].

Clay Minerals Content
It's well known that the clay minerals content could affect the AGC [7, 29,67]. In this study, Va and clay mineral content are positively correlated (Figure 12), this is maybe due to the increase of clay minerals increasing the PD and SSA of shale to some extent, so the adsorption capacity and adsorbed gas content is also increased. Gu et al. [29] further described the adsorption characteristics of main components of clay minerals and concluded that the Montmorillonite has the maximum adsorption capacity, followed by Kaolinite and Chlorite, while illite has the least.

Brittle Mineral Content
The brittle minerals in shale mainly include quartz, orthoclase, anorthose, etc. Due to the small SSA, the adsorption capacity of them is generally weak, but the existence of brittle minerals is propitious to produce cracks and fractures, which indirectly affect the AGC. Then, the relationship between V a and brittle mineral content in the studied shale samples shows that they are negatively correlated (Figure 13a), because the adsorption positions for gas molecules that shale can provide also decrease.
Meanwhile, quartz is found to be the most important composition in brittle minerals, and the effect of feldspar content is little in comparison with it.

Brittle Mineral Content
The brittle minerals in shale mainly include quartz, orthoclase, anorthose, etc. Due to the small SSA, the adsorption capacity of them is generally weak, but the existence of brittle minerals is propitious to produce cracks and fractures, which indirectly affect the AGC. Then, the relationship between Va and brittle mineral content in the studied shale samples shows that they are negatively correlated (Figure 13a), because the adsorption positions for gas molecules that shale can provide also decrease. Meanwhile, quartz is found to be the most important composition in brittle minerals, and the effect of feldspar content is little in comparison with it.

Carbonate Mineral Content
Carbonate minerals in shale samples include calcite, dolomite, dolomite and siderite, among which, calcite is the most important component. From Figure 14a, it can be seen that, Va and carbonate content have the inverse correlation relations. The reason may be that carbonate minerals are often as cementation existed in micro pores or fractures, which occupy part surface area, lower the AGC in shales, resulting in the negative correlation between Va and carbonate content. However, calcite has no obvious effect on AGC, this maybe relates to it being missing in partial samples (Figure 14b).

Other Mineral Content
In the other minerals, the content of pyrite can't be neglected in this study (2.8%-5.3%). Previous research has confirmed that there is a coexistent relationship between pyrite and OM as well as AGC [5,6,33,68]. However, Va has no indistinctive positive correlation with pyrite content for the studied shale samples (Figure 15). This is mainly because iron is the primary material for OM deposition, high iron content makes for OM enriching, but the lacustrine environment where the Yanchang shale

Carbonate Mineral Content
Carbonate minerals in shale samples include calcite, dolomite, dolomite and siderite, among which, calcite is the most important component. From Figure 14a, it can be seen that, V a and carbonate content have the inverse correlation relations. The reason may be that carbonate minerals are often as cementation existed in micro pores or fractures, which occupy part surface area, lower the AGC in shales, resulting in the negative correlation between V a and carbonate content. However, calcite has no obvious effect on AGC, this maybe relates to it being missing in partial samples (Figure 14b).

Brittle Mineral Content
The brittle minerals in shale mainly include quartz, orthoclase, anorthose, etc. Due to the small SSA, the adsorption capacity of them is generally weak, but the existence of brittle minerals is propitious to produce cracks and fractures, which indirectly affect the AGC. Then, the relationship between Va and brittle mineral content in the studied shale samples shows that they are negatively correlated (Figure 13a), because the adsorption positions for gas molecules that shale can provide also decrease. Meanwhile, quartz is found to be the most important composition in brittle minerals, and the effect of feldspar content is little in comparison with it.

Carbonate Mineral Content
Carbonate minerals in shale samples include calcite, dolomite, dolomite and siderite, among which, calcite is the most important component. From Figure 14a, it can be seen that, Va and carbonate content have the inverse correlation relations. The reason may be that carbonate minerals are often as cementation existed in micro pores or fractures, which occupy part surface area, lower the AGC in shales, resulting in the negative correlation between Va and carbonate content. However, calcite has no obvious effect on AGC, this maybe relates to it being missing in partial samples (Figure 14b).

Other Mineral Content
In the other minerals, the content of pyrite can't be neglected in this study (2.8%-5.3%). Previous research has confirmed that there is a coexistent relationship between pyrite and OM as well as AGC [5,6,33,68]. However, Va has no indistinctive positive correlation with pyrite content for the studied shale samples (Figure 15). This is mainly because iron is the primary material for OM deposition, high iron content makes for OM enriching, but the lacustrine environment where the Yanchang shale

Other Mineral Content
In the other minerals, the content of pyrite can't be neglected in this study (2.8-5.3%). Previous research has confirmed that there is a coexistent relationship between pyrite and OM as well as AGC [5,6,33,68]. However, V a has no indistinctive positive correlation with pyrite content for the studied shale samples (Figure 15). This is mainly because iron is the primary material for OM deposition, high iron content makes for OM enriching, but the lacustrine environment where the Yanchang shale deposited is not conducive to the formation of pyrite, the content of pyrite is not at the concentrations observed in marine systems [10]. deposited is not conducive to the formation of pyrite, the content of pyrite is not at the concentrations observed in marine systems [10].

Pore-Based Influence Factors
Shale reservoirs are characterized by nano-micro pores and very low permeability. It is a challenge to detect and quantify these small pores. In this study, parameters including PD, SSA, PV as well as porosity and permeability are used to show the influence of pore-based factors.

Pore Size Distribution
For the shale samples, PD is 15.7-21.7 nm and the median radius is 7.8-10.9 nm. From Figure 16, we can find that there is no obvious relationship between Va and pore size parameters, including both average PD and median radius, which indicates the pore size distribution is not the main influence factor of AGC.

Specific Surface Area
Previous study concluded that SSA was one of the main controlling factors on adsorption behaviors [17]. In this study, SSA calculated by BET and BJH models has no obvious effect on AGC (Va has no obvious correlation with SSA, Figure 17). The reason may be that these shale samples are in the mature stages, and the existence of residual oil will occupy part of the adsorption sites for gas molecules, which could retrain the shale gas adsorption capacity to some extent.

Pore-Based Influence Factors
Shale reservoirs are characterized by nano-micro pores and very low permeability. It is a challenge to detect and quantify these small pores. In this study, parameters including PD, SSA, PV as well as porosity and permeability are used to show the influence of pore-based factors.

Pore Size Distribution
For the shale samples, PD is 15.7-21.7 nm and the median radius is 7.8-10.9 nm. From Figure 16, we can find that there is no obvious relationship between V a and pore size parameters, including both average PD and median radius, which indicates the pore size distribution is not the main influence factor of AGC. deposited is not conducive to the formation of pyrite, the content of pyrite is not at the concentrations observed in marine systems [10].

Pore-Based Influence Factors
Shale reservoirs are characterized by nano-micro pores and very low permeability. It is a challenge to detect and quantify these small pores. In this study, parameters including PD, SSA, PV as well as porosity and permeability are used to show the influence of pore-based factors.

Pore Size Distribution
For the shale samples, PD is 15.7-21.7 nm and the median radius is 7.8-10.9 nm. From Figure 16, we can find that there is no obvious relationship between Va and pore size parameters, including both average PD and median radius, which indicates the pore size distribution is not the main influence factor of AGC.

Specific Surface Area
Previous study concluded that SSA was one of the main controlling factors on adsorption behaviors [17]. In this study, SSA calculated by BET and BJH models has no obvious effect on AGC (Va has no obvious correlation with SSA, Figure 17). The reason may be that these shale samples are in the mature stages, and the existence of residual oil will occupy part of the adsorption sites for gas molecules, which could retrain the shale gas adsorption capacity to some extent.

Specific Surface Area
Previous study concluded that SSA was one of the main controlling factors on adsorption behaviors [17]. In this study, SSA calculated by BET and BJH models has no obvious effect on AGC (V a has no obvious correlation with SSA, Figure 17). The reason may be that these shale samples are in the mature stages, and the existence of residual oil will occupy part of the adsorption sites for gas molecules, which could retrain the shale gas adsorption capacity to some extent.

Pore Volume
Results show that, there is no significant linear relationship between Va and PV whether calculated based on BET model or BTH model ( Figure 18). Because the OM pores are not developed due to the low thermal evolution maturity, and intergranular pores are the dominant pore types in the total Yanchang shales pores, which that is not in favor of shale gas adsorption.

Porosity
Some previous studies considered porosity as having a positive correlation with TOC content for thermally overmature marine shales [3]. However, for the thermally early-mature Yanchang shale with strong reservoir heterogeneity, the range of porosity variation is relatively large about 3.3-4.5%, and the effect of physical property on AGC is more complex, so the linear relationship between Va and porosity is not obvious (Figure 19). In general, the porosity has little effect on the AGC in shales.

Pore Volume
Results show that, there is no significant linear relationship between V a and PV whether calculated based on BET model or BTH model ( Figure 18). Because the OM pores are not developed due to the low thermal evolution maturity, and intergranular pores are the dominant pore types in the total Yanchang shales pores, which that is not in favor of shale gas adsorption.

Pore Volume
Results show that, there is no significant linear relationship between Va and PV whether calculated based on BET model or BTH model ( Figure 18). Because the OM pores are not developed due to the low thermal evolution maturity, and intergranular pores are the dominant pore types in the total Yanchang shales pores, which that is not in favor of shale gas adsorption.

Porosity
Some previous studies considered porosity as having a positive correlation with TOC content for thermally overmature marine shales [3]. However, for the thermally early-mature Yanchang shale with strong reservoir heterogeneity, the range of porosity variation is relatively large about 3.3-4.5%, and the effect of physical property on AGC is more complex, so the linear relationship between Va and porosity is not obvious (Figure 19). In general, the porosity has little effect on the AGC in shales.

Porosity
Some previous studies considered porosity as having a positive correlation with TOC content for thermally overmature marine shales [3]. However, for the thermally early-mature Yanchang shale with strong reservoir heterogeneity, the range of porosity variation is relatively large about 3.3-4.5%, and the effect of physical property on AGC is more complex, so the linear relationship between V a and porosity is not obvious (Figure 19). In general, the porosity has little effect on the AGC in shales.

Permeability
The permeability of shale reflects the development degree of fractures to some extent [58]. From Figure 20, it can be seen that, Va and permeability only have no significant relationship. This shows that the permeability had little effect on the AGC. From the other aspect, Jia et al. [58] concluded that the gas adsorption layer may reduce the permeability by reducing the effective pore size, but the effect is limited, indicating the mysterious influence of permeability.

External Influence Factors
The above factors can be seen as internal factors affecting AGC in shales, while some external factors such as temperature, pressure and humidity are also important.

Temperature
By conducting the CH4 isothermal adsorption experiments at different temperatures on the same shale sample, the influence of temperature on the adsorption process and AGC can be analyzed [17,69]. In this study, Sample 7 was carried out the experiments at 30, 60, 90 and 120 °C, respectively, the experimental pressure increased from 0 MPa to about 17 MPa, until the measured adsorption gas content was no longer increased. After fitting the experimental data, the isothermal adsorption curves at different temperatures can be obtained, shown in Figure 21a, which shows the increase of

Permeability
The permeability of shale reflects the development degree of fractures to some extent [58]. From Figure 20, it can be seen that, V a and permeability only have no significant relationship. This shows that the permeability had little effect on the AGC. From the other aspect, Jia et al. [58] concluded that the gas adsorption layer may reduce the permeability by reducing the effective pore size, but the effect is limited, indicating the mysterious influence of permeability.

Permeability
The permeability of shale reflects the development degree of fractures to some extent [58]. From Figure 20, it can be seen that, Va and permeability only have no significant relationship. This shows that the permeability had little effect on the AGC. From the other aspect, Jia et al. [58] concluded that the gas adsorption layer may reduce the permeability by reducing the effective pore size, but the effect is limited, indicating the mysterious influence of permeability.

External Influence Factors
The above factors can be seen as internal factors affecting AGC in shales, while some external factors such as temperature, pressure and humidity are also important.

Temperature
By conducting the CH4 isothermal adsorption experiments at different temperatures on the same shale sample, the influence of temperature on the adsorption process and AGC can be analyzed [17,69]. In this study, Sample 7 was carried out the experiments at 30, 60, 90 and 120 °C, respectively, the experimental pressure increased from 0 MPa to about 17 MPa, until the measured adsorption gas content was no longer increased. After fitting the experimental data, the isothermal adsorption curves at different temperatures can be obtained, shown in Figure 21a, which shows the increase of

External Influence Factors
The above factors can be seen as internal factors affecting AGC in shales, while some external factors such as temperature, pressure and humidity are also important.

Temperature
By conducting the CH 4 isothermal adsorption experiments at different temperatures on the same shale sample, the influence of temperature on the adsorption process and AGC can be analyzed [17,69]. In this study, Sample 7 was carried out the experiments at 30, 60, 90 and 120 • C, respectively, the experimental pressure increased from 0 MPa to about 17 MPa, until the measured adsorption gas content was no longer increased. After fitting the experimental data, the isothermal adsorption curves at different temperatures can be obtained, shown in Figure 21a, which shows the increase of temperature could inhibit the adsorption of shale gas to a certain extent. In order to directly express the relationship between AGC and temperature, V a and T of shale sample 7 are fitted, from Figure 21b we can conclude that, a negative linear correlation is really existed between V a and T. temperature could inhibit the adsorption of shale gas to a certain extent. In order to directly express the relationship between AGC and temperature, Va and T of shale sample 7 are fitted, from Figure  21b we can conclude that, a negative linear correlation is really existed between Va and T. Pressure Given a certain experimental temperature, conduct the CH4 isothermal adsorption experiment for a shale sample, the isothermal adsorption curves of can be obtained, shown as Figure 22. Results show that the measured AGC has a logarithmical relationship with pressure, when the pressure increases to a certain value, the increasing extent of AGC is not obvious and heads for a specific value VL on behalf of the maximum shale gas adsorption capacity. The reason is that when the pressure is low, the gas molecules require higher binding energy to be adsorbed, with the pressure increasing, the binding energy they required gradually reduces, the adsorption capacity gradually increases instead [70,71].

Humidity
Previous studies show that the effective adsorption sites for the gas molecules are fixed numbers in the inner surface of the coal, when water content or humidity in the coal is high, some adsorption sites can be occupied by the water molecules, thus adsorption sites for the gas molecules will decrease [21,72]. In this study, isothermal adsorption experiments were conducted under conditions of various humidity in shale samples (Table 1), through analysis it can be seen that, Va has no obvious correlation with humidity, indicating humidity has little effect on the AGC in Yanchang shales (Figure 23). Pressure Given a certain experimental temperature, conduct the CH 4 isothermal adsorption experiment for a shale sample, the isothermal adsorption curves of can be obtained, shown as Figure 22. Results show that the measured AGC has a logarithmical relationship with pressure, when the pressure increases to a certain value, the increasing extent of AGC is not obvious and heads for a specific value V L on behalf of the maximum shale gas adsorption capacity. The reason is that when the pressure is low, the gas molecules require higher binding energy to be adsorbed, with the pressure increasing, the binding energy they required gradually reduces, the adsorption capacity gradually increases instead [70,71]. temperature could inhibit the adsorption of shale gas to a certain extent. In order to directly express the relationship between AGC and temperature, Va and T of shale sample 7 are fitted, from Figure  21b we can conclude that, a negative linear correlation is really existed between Va and T. Pressure Given a certain experimental temperature, conduct the CH4 isothermal adsorption experiment for a shale sample, the isothermal adsorption curves of can be obtained, shown as Figure 22. Results show that the measured AGC has a logarithmical relationship with pressure, when the pressure increases to a certain value, the increasing extent of AGC is not obvious and heads for a specific value VL on behalf of the maximum shale gas adsorption capacity. The reason is that when the pressure is low, the gas molecules require higher binding energy to be adsorbed, with the pressure increasing, the binding energy they required gradually reduces, the adsorption capacity gradually increases instead [70,71].

Humidity
Previous studies show that the effective adsorption sites for the gas molecules are fixed numbers in the inner surface of the coal, when water content or humidity in the coal is high, some adsorption sites can be occupied by the water molecules, thus adsorption sites for the gas molecules will decrease [21,72]. In this study, isothermal adsorption experiments were conducted under conditions of various humidity in shale samples (Table 1), through analysis it can be seen that, Va has no obvious correlation with humidity, indicating humidity has little effect on the AGC in Yanchang shales ( Figure 23).

Humidity
Previous studies show that the effective adsorption sites for the gas molecules are fixed numbers in the inner surface of the coal, when water content or humidity in the coal is high, some adsorption sites can be occupied by the water molecules, thus adsorption sites for the gas molecules will decrease [21,72]. In this study, isothermal adsorption experiments were conducted under conditions of various humidity in shale samples (Table 1), through analysis it can be seen that, V a has no obvious correlation with humidity, indicating humidity has little effect on the AGC in Yanchang shales ( Figure 23).

Influence Factors Optimization
From the above analysis, the AGC in shale is comprehensively affected by various geological factors to various degrees. The factors consist of internal factors, including geochemical factors, mineral compositions, pore-based characteristics, and external factors. However, not all the geological factors are significantly correlated with the AGC. Generally, when the absolute value of the correlation coefficient |R| is larger than 0.5, it can be considered that the two variables are relatively highly correlated. Only if |R| is greater than the critical value do the influence factors really have statistical significance. Therefore, the significant influence factors can tell from the 12 geological factors displayed in Table 2.
Then we fit the linear relationships between the single influence factor and Va. The regression equations and the coefficient of determination (R 2 ) are shown in Figures 7, 8, 10, 12, 13a,b, 14a, 21b, and 22. For the influence of single factor, R 2 of Va with TOC, S1, S2, S1 + S2, chloroform bitumen "A", clay minerals content, quartz content, brittle minerals content, carbonate minerals content, pressure and temperature is generally low with values of 0.52, 0.60, 0.50, 0.54, 0.51, 0.48, 0.30, 0.28, 0.32, 0.39, 0.39, respectively. The results indicate that the AGC cannot be predicted with only one significant geological factor, thus establishing a multi-factor regression model to quantitatively predict the AGC is necessary.
To establish the mathematical prediction model, not all the above influence factors are useful. In this study, we optimized the main influence factors based on two principles that (1) significant correlation, |R| > 0.5 and Sig. < 0.05, these factors are displayed in Table 2; (2) no obvious relationship among the single factors, such as both chloroform bitumen "A" and S1 represent the residual hydrocarbons, so only S1 is selected; quartz is the main composition of brittle minerals, so quartz content instead of brittle content is optimized. Therefore, eight factors including TOC, S1, S2, Clm, Qz, Cam, T as well as lnP, are thought as the main influence factors affecting the AGC of the Yanchang Formation, which are further used to predict the content.

Influence Factors Optimization
From the above analysis, the AGC in shale is comprehensively affected by various geological factors to various degrees. The factors consist of internal factors, including geochemical factors, mineral compositions, pore-based characteristics, and external factors. However, not all the geological factors are significantly correlated with the AGC. Generally, when the absolute value of the correlation coefficient |R| is larger than 0.5, it can be considered that the two variables are relatively highly correlated. Only if |R| is greater than the critical value do the influence factors really have statistical significance. Therefore, the significant influence factors can tell from the 12 geological factors displayed in Table 2.
Then we fit the linear relationships between the single influence factor and V a . The regression equations and the coefficient of determination (R 2 ) are shown in Figure 7, Figure 8, Figure 10, Figure 12, Figure 13a,b, Figure 14a, Figure 21b, and Figure 22. For the influence of single factor, R 2 of V a with TOC, S 1 , S 2 , S 1 + S 2 , chloroform bitumen "A", clay minerals content, quartz content, brittle minerals content, carbonate minerals content, pressure and temperature is generally low with values of 0.52, 0.60, 0.50, 0.54, 0.51, 0.48, 0.30, 0.28, 0.32, 0.39, 0.39, respectively. The results indicate that the AGC cannot be predicted with only one significant geological factor, thus establishing a multi-factor regression model to quantitatively predict the AGC is necessary.
To establish the mathematical prediction model, not all the above influence factors are useful. In this study, we optimized the main influence factors based on two principles that (1) significant correlation, |R| > 0.5 and Sig. < 0.05, these factors are displayed in Table 2; (2) no obvious relationship among the single factors, such as both chloroform bitumen "A" and S 1 represent the residual hydrocarbons, so only S 1 is selected; quartz is the main composition of brittle minerals, so quartz content instead of brittle content is optimized. Therefore, eight factors including TOC, S 1 , S 2 , Clm, Q z , Cam, T as well as lnP, are thought as the main influence factors affecting the AGC of the Yanchang Formation, which are further used to predict the content.

Establish Mathematical Prediction Model
Regression analysis is a statistical process for estimating the relationships among variables, which includes many techniques for modeling and analyzing several variables. It is widely used for forecasting and understanding that which is related to the dependent variable among the independent variables and exploring the correlative equations between them. In this study, prediction models established are multiple linear regression functions, and the optimized eight main influence factors are entered as the independent variables. Nowadays some specialized regression software has been widely used, such as the SPSS software, which packages perform multiple linear regression using least squares, the methods cover enter, stepwise, remove, forward and backward, among which, backward is proved the most effective method used for the studied shale samples. The backward regression method is to input all the independent variables first, then eliminate a variable that does not conform to the entry criteria each time, until the regression function no longer contains an independent variable that is not consistent with the criteria.
In Tables 3-5, five models are established according to the backward method, R 2 of each model is high up to 0.8 with small error, thus each model can be used as the prediction model, in this case, we give preference to Model 4 which has less independent variables (R is 0.885, shown in Table 4). In this model, the geological factors used to predict the AGC are TOC, S 1 , quartz content and T, coefficients of each factor can be obtained from Table 5, the prediction function is as Equation (2): where, V a refers to the predicted AGC, m 3 /t; TOC refers to the total organic carbon content, wt %; S 1 refers to the residual hydrocarbon content, %; Q z refers to quartz content, wt %; T refers to the formation temperature, • C.

Reliability Test of the Prediction Model
To verify the models' reliability, we obtained the predicted AGC by entering the measured values of variables TOC, S 1 , Q z and T into the prediction model Equation (2), then made a correlation analysis and error analysis of the experimental values and the predicted AGC. It can be seen that, the predicted AGC has significant correlation with the experimental content, with R high up to 0.897 (Figure 24a) and the relative error less than ±20% (Figure 24b), proving that the prediction model is reliable, which can be used to effectively forecast the AGC in Yanchang shales.
The multi-factor regression analysis method used in this study is of significant guidance for calculating AGC continuously. However, when apply this method to other shale reservoir, more detailed work is required and necessary to confirm the accurate relationships between AGC and influence factors, and then the targeted prediction models can be established.

Reliability Test of the Prediction Model
To verify the models' reliability, we obtained the predicted AGC by entering the measured values of variables TOC, S1, Qz and T into the prediction model Equation (2), then made a correlation analysis and error analysis of the experimental values and the predicted AGC. It can be seen that, the predicted AGC has significant correlation with the experimental content, with R high up to 0.897 ( Figure 24a) and the relative error less than ± 20% (Figure 24b), proving that the prediction model is reliable, which can be used to effectively forecast the AGC in Yanchang shales.
The multi-factor regression analysis method used in this study is of significant guidance for calculating AGC continuously. However, when apply this method to other shale reservoir, more detailed work is required and necessary to confirm the accurate relationships between AGC and influence factors, and then the targeted prediction models can be established.

Comparison of AGC Obtained from Different Methods
In this study, AGC in shales was obtained by three methods, including the CH4 isothermal adsorption experimental method, the single-factor correlation analysis method, and the multi-factor regression analysis method. In general, the AGC has an overall increasing trend with the increase of burial depth, unanimous for different methods (Figure 24). Except the single-factor correlation analysis, the other two methods are significantly correlated, so they are further compared. As shown in Table 6, every method naturally has advantages and disadvantages, and can complement each other when evaluating the AGC in shales.

Comparison of AGC Obtained from Different Methods
In this study, AGC in shales was obtained by three methods, including the CH 4 isothermal adsorption experimental method, the single-factor correlation analysis method, and the multi-factor regression analysis method. In general, the AGC has an overall increasing trend with the increase of burial depth, unanimous for different methods (Figure 24). Except the single-factor correlation analysis, the other two methods are significantly correlated, so they are further compared. As shown in Table 6, every method naturally has advantages and disadvantages, and can complement each other when evaluating the AGC in shales. The CH 4 isothermal adsorption experimental method is the most direct and effective way to evaluate the AGC, including the actual AGC and the maximum adsorption capacity based on the Langmuir equation. However, it is greatly restricted by the economic factors due to the high testing cost, unable to effectively apply to large scale samples and wells. For the multi-factor regression analysis method, AGC predicted by this method varies continuously because of the spatial extension of geological factors in shale formation, not just restricted by one single well. However, due to the less core sample numbers (N = 15), the accuracy of prediction model needs improvements, if larger core numbers are satisfied, this method will have more universal applicability at the mature exploration area [9,36].

Evaluation of Shale Gas Adsorption Capacity of Yanchang Formation
As discussed above, for the AGC in Yanchang shales, the on-site analytical gas amounts are in the range of 1.24-2.34 m 3 /t with a mean of 1.68 m 3 /t, the actual gas absorption quantity (V a ) is in the range of 0.83-1.60 m 3 /t with a mean of 1.15 m 3 /t, indicating that the studied shale samples have a moderate gas adsorption capacity though generally lower than the marine Longmaxi shales [10]. Except for the adsorbed gas, free gas and dissolved gas are also the important occurrence states of shale gas [73]. Our previous studies have concluded that Yanchang shales have the characteristics of primary adsorbed gas (44-65%, average 58%), moderate free gas (24-45%; average 32%), and non-ignorable dissolved gas (10%) [10]. Evaluating shale gas occurrence forms and their proportions in shales especially the adsorbed one is curial to both exploration and exploitation. It is not only vital for evaluating shale gas resource potential, but also the key to analyzing production capacity, gas reservoir types, and exploitation method [74].
Shale gas exploitation mainly involves the adsorbed gas and free gas. In general, free gas content determines the primary productivity, while adsorbed gas content determines the stable production time [75,76]. Gas reservoirs with high free gas content have a higher initial recovery rate, which is conducive to economic development [75]. For Yanchang shales, adsorbed gas is the primary occurrence form, and free gas follows. The dissolved gas can also transform into free gas during exploitation, indicating that the primary productivity in the continental shale maybe low, but its stable production time may be long and thus determining the final gas production because of its larger adsorption gas ratio. The predicted trend of gas production for the Yanchang shale is similar to the gas production rate and decline trend in the Barnett Shale [77]. The initial gas production rate in the Barnette Shale is generally lower than other major shales in the US like Eagle ford, Marcellus and Haynesville [77]. Mavor [78] revealed it was due to the adsorption gas content of the Barnette Shale occupied about 60% of the total shale gas reserves.
The exploration and production of continental shale gas in China are mainly concentrated in the Ordos Basin, numbers of shale gas wells have been drilled and some of them have been successfully fractured. Among which, well LP177 was the first continental shale horizontal well in China and achieved a shale gas flow of 2000 m 3 /d after fracturing. So far, plenty of wells have obtained industrial gas flow with the average gas content of 4000 m 3 /d, and fracturing technology has also made breakthroughs and innovative progress. The great shale gas content and industrial gas flow demonstrate the large potential of shale gas resource in Yanchang formation in the Ordos Basin, also indicate a great prospect for further exploration and development [25]. Though the primary production of continental shale gas is usually lower than that of predicted gas potential especially when compared to marine shale gas, the stable production time maybe longer, and with the advance of fracturing technology, the scale production can be expected in the future. Based on the above analysis, it can be concluded that under the present natural gas market, it is more economical to preferentially exploit marine shale gas than continental shale gas, the resource potential of continental shales, however, can be still substantial and should not be belittled.

Conclusions
The influence of geological factors on the AGC in the Yanchang shales has been analyzed based on laboratory experiments and correlation analysis then, a mathematical prediction model has been established according to the multi-factor regression analysis. Conclusions are as follows: (1) Adsorbed gas content (AGC) in the Yanchang shales is comprehensively affected by various geological factors to different degrees. TOC, S 1 , S 2 , S 1 + S 2 , chloroform bitumen "A" and clay minerals content have significant correlations with V a at the level of 0.01 (bilateral). Quartz content, brittle minerals content, carbonate minerals content, pressure and temperature have significant correlations with V a at the level of 0.05 (bilateral). However, the coefficient of determination (R 2 ) of the fitted equations between the single influence actor and V a is overall lower than 0.5, can't be used to predict the shale gas adsorption amount.
(2) A mathematical predication model of the AGC in Yanchang shales has been established by the statistical method multi-factor regression analysis based on the SPSS software, which is the function of TOC, S 1 , quartz content and formation temperature. The reliability of the predicted AGC obtained from the prediction model is verified by the actual values obtained from the CH 4 isothermal adsorption experiment, with the coefficient of determination as high as 0.8046 and the relative error less than ±20%.
(3) AGC in Yanchang shales obtained from the CH 4 isothermal adsorption experimental method, the single-factor correlation analysis method, and the multi-factor regression analysis method all have a general increasing trend with the increasing depth, though unanimous for different methods. Comparison supports that CH 4 isothermal adsorption experimental method is the most direct and effective way to evaluate the AGC of one sample, while AGC predicted by the multi-factor regression analysis method can vary continuously and have general applicability at mature exploration area.
(4) Comprehensive assessment of shale gas adsorption capacity of Yanchang formation indicates, there is an overall moderate gas adsorption capacity. Among the three occurrence forms, the adsorbed gas occupies about 58% of the total, determining the stable production time of the Yanchang shale reservoir. Though under the present conditions, the economic benefits of the continental shale gas are not obvious, the resource potential of Yanchang formation cannot be ignored.