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Article

A Model and the Characteristics of Gas Generation of the Longmaxi Shale in the Sichuan Basin

by
Xuewen Shi
1,
Yi Li
1,
Yuqiang Jiang
2,3,*,
Ye Zhang
4,
Wei Wu
1,
Zhiping Zhang
4,
Zhanlei Wang
2,3,*,
Xingping Yin
2,3,
Yonghong Fu
2,3 and
Yifan Gu
2,3
1
Shale Gas Research Institute, PetroChina Southwest Oil & Gasfield Company, Chengdu 610000, China
2
School of Geoscience and Technology, Southwest Petroleum University, Chengdu 610500, China
3
Petrochina Key Laboratory of Unconventional Oil and Gas, Reservoir Evaluation Branch, Southwest Petroleum University, Chengdu 610500, China
4
National and Local Joint Engineering Research Center of Shale Gas Exploration and Development (Chongqing Institute of Geology and Mineral Resources), Chongqing 401120, China
*
Authors to whom correspondence should be addressed.
Processes 2025, 13(7), 2294; https://doi.org/10.3390/pr13072294
Submission received: 15 May 2025 / Revised: 26 June 2025 / Accepted: 11 July 2025 / Published: 18 July 2025
(This article belongs to the Special Issue Advances in Theory and Technology of Unconventional Oil and Gas Reservoirs)
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Abstract

Currently, the Longmaxi shale in the Sichuan Basin is the most successful stratum of shale gas production in China. However, because Longmaxi shale mostly has high over-maturity, a low-maturity sample cannot be obtained for gas generation thermal simulations, and as a result, a gas generation model has not yet been established for it. Therefore, models of other shales are usually used to calculate the amount of gas generated from Longmaxi shale, but they may produce inaccurate results. In this study, a Longmaxi shale sample with an equivalent vitrinite reflectance calculated from Raman spectroscopy (EqVRo) of 1.26% was obtained from Well Yucan 1 in the Chengkou area, northeast Sichuan Province. This Longmaxi shale may have the lowest maturity in nature. Pyrolysis simulations based on gold tubes were performed on this sample, and the gas generation line was obtained. The amount of gas generated during the low-maturity stage was compensated by referring to gas generation data obtained from Lower Silurian black shale in western Lithuania. Thus, a gas generation model of the Longmaxi shale was built. The model showed that the gas generation process of Longmaxi shale could be divided into three stages: (1) First, there is the quick generation stage (EqVRo 0.5–3.0%), where hydrocarbon gases were generated quickly and constantly, and the generation rate was steady. A maximum of 458 mL/g TOC was reached at a maturity of 3.0% EqVRo. (2) Second, there is the stable stage (EqVRo 3.0–3.25%), where the amount of generated gas reached a plateau of 453–458 mL/g TOC. (3) Third, there is the rapid descent stage (EqVRo > 3.25%), where the amount of generated gas started to decrease, and it was 393 mL/g TOC at an EqVRo of 3.34%. This model allows us to more accurately calculate the amount of gas generated from the Longmaxi shale in the Sichuan Basin.

1. Introduction

The amount of gas generated is an important parameter in the resource evaluation of shale gas, and it is usually calculated from the gas generation model and the thermal history of the studied shale. The general model of hydrocarbon generation suggests that oil is produced when the maturity of organic matter is in the vitrinite reflectance (Ro) range of 0.5–1.3%, and gas is the dominant product when Ro is higher than 1.3% [1]. In the high-maturity stage (Ro > 1.3%), the amount of gas produced from kerogen is limited, while gas produced from residual liquid hydrocarbons can account for ~70% of the gas generated in this stage [2]. Hill et al. [3] pointed out that the gas generated from residual liquid hydrocarbons was mostly in an Ro range of 1.3–2.0%. In commercial software for basin modeling (e. g., PetroMod, BasinMod, and BASIMS), different types of kinetic models of hydrocarbon generation can be used to calculate the amount of generated hydrocarbons by substituting the models into the thermal history of source rocks [4,5,6]. However, all of these models were established based on thermal simulations of typical source rocks with a low maturity, and these source rocks may differ from others in terms of hydrocarbon generation. Therefore, a specific kinetic model of the studied source rock can allow researchers to more precisely determine the amount of hydrocarbon generated.
In the thermal simulation of hydrocarbon generation, a source rock or kerogen is heated at different times and temperatures to generate hydrocarbons, and the amounts of hydrocarbons produced are recorded throughout the heating process [7,8,9,10]. In terms of samples, bulk source rocks and kerogen can be used. A small amount of kerogen can produce a lot of hydrocarbons; thus, the products can be quantified precisely. However, inorganic minerals, such as clays, may catalyze hydrocarbon generation [11]. Therefore, it is better to use bulk source rocks to investigate hydrocarbon generation models because the influence of inorganic minerals can be considered. Three types of systems can be used for the thermal simulation of hydrocarbon generation: open, closed, and semi-open systems. Among them, in open systems such as Rock-Eval, powered bulk shale samples (60–80 mesh) are heated, and the generated hydrocarbons are quantified using a flame ionization detector (FID). However, although this system is efficient and low-cost, it cannot be used to investigate the oil-cracking reaction [12]; thus, the gas generation capacity may be underestimated. In closed systems, such as gold tubes, the pressure can be considered, and the precise amounts of products can be determined; therefore, they are widely used to investigate hydrocarbon generation [10]. In gold tube systems, powered samples are heated under pressures similar to those of the subsurface, and all products, including oils and gases, are sealed in the tubes. Finally, the generated oils and gases are quantified by piercing the tubes using a needle in the high vacuum line. In semi-open systems, the in situ pyrolysis of organic matter in source rocks can be simulated [13], and both bulk samples and powered samples can be used. Hydrostatic and lithostatic pressures can be considered at the same time. Therefore, semi-open systema are considered as the best choice for simulating subsurface conditions; however, they are time-consuming and expensive. The kinetic parameters, including activation energy and frequency factor, are usually obtained according to the Arrhenius equation based on experimental results with two or more heating rates.
In this study, to precisely determine the amount of gas generated and consider the pressure, thermal simulations were performed on bulk shale using gold tubes. Usually, the pressure is kept stable in thermal simulations using gold tubes [14]; however, the pressure actually increases with an increasing burial depth. Therefore, to more accurately simulate the geological situation, different hydrostatic pressures were set according to the heating temperature. Although many previous studies found that water played an important role in hydrocarbon generation reactions [7,15], the kinetic parameters from hydrous and anhydrous pyrolysis experiments were similar [16,17], implying that water actually played a small role [18]. The previous experiments showed that the water generation ended when the vitrinite reflectance reached 1.2% [19]. The water content in shales decreased significantly when the equivalent vitrinite reflectance was greater than 1.5% [20]. Therefore, we speculated that water played a small role in gas generation at the highly mature stage due to its low content and conducted anhydrous thermal simulations.
Longmaxi shale is currently the most successful stratum for shale gas exploration and production in China. To date, three national marine shale gas demonstration areas have been established, namely Jiaoshiba, Weiyuan-Changning, and Zhaotong, and many shale gas fields have been discovered [21]. However, there is no specific gas generation model for Longmaxi shale because its equivalent vitrinite reflectance values are mainly in the range of 2.0–4.0%, which cannot be used to conduct thermal simulations of gas generation [22]. In previous studies, the amount of gas generated from Longmaxi shale was calculated using gas generation models of other shales, such as the Xiamaling and Baltic shales [23]. It should be noted that the chemical composition of the organic matter in these shales and the Longmaxi shale may differ; thus, the calculated results are probably unreliable. Therefore, to establish a specific gas generation model for Longmaxi shale, seeking shale samples with a relatively low maturity from the Longmaxi Formation is crucial.
The Longmaxi shale from Well Yucan 1 has a relatively low maturity (~1.3%Ro) [24]; thus, a gas generation model for Longmaxi shale could be established by conducting thermal simulations on samples from this well. Consequently, in this study, we obtained a Longmaxi shale sample from Well Yucan 1 and conducted thermal simulations on it using gold tubes. The gas generation line was measured, and then the amount of gas generated in the vitrinite reflectance range of 0.5–1.3% was determined according to data obtained from a lower Silurian shale sample in the western region of Lithuania. A gas generation model of Longmaxi shale was finally established, and this model can be used to precisely calculate the amount of gas generated from Longmaxi shale.

2. Sample and Methods

2.1. Sample

Longmaxi shale was deposited in the period of marine transgression during the Early Silurian [25]. The sedimentary depocenters are in the southern Sichuan Basin (Figure 1). Its thickness varies from 20 m to 100 m in the southern and eastern Sichuan Basin (Figure 1). The dominant lithologies of this shale are siliceous and carbonaceous shales. The Longmaxi Formation is divided into two sections according to its lithological composition. Section 1 is in the lower part and is mainly composed of organics-rich black carbonaceous shale, siliceous shale, and dark gray arenaceous shale; graptolites are abundant [22]. Section 2 is in the upper part and is mainly composed of grayish-yellowish green shale and arenaceous shale interbedded with siltstone and marlstone. Section 1 is further divided into two layers. Layer 1 (Long11) is in the lower part of Section 1 and is predominantly composed of black shale, and it is divided into four sub-layers of 1–4 from the bottom to the top. Among them, sub-layer 1 is composed of organics-rich black shale and is the main gas-producing layer of Longmaxi shale. Well Yucan 1 is located in the northeast of the Sichuan Basin (Figure 1), and it drills the Upper Ordovician and Lower Silurian strata (Figure 2). The sample used in this study was obtained from sub-layer 1 of layer 1 in Section 1 of the Longmaxi Formation at 1202.85 m–1202.92 m of Well Yucan 1, Chengkou, Chongqing (Figure 1, Figure 2 and Figure 3).

2.2. Methods

Each sample was crushed into 100 mesh powders with a pestle and mortar. Then, pyrolysis parameters and TOC content in the powdered samples were measured using a Rock-Eval 6 instrument manufactured by Vinci Technologies, Paris, France, and a CS-230 Elemental Analyzer made by LECO, Michigan, USA, according to the methods described by Han et al. [27]. Raman spectra were measured using an inVia Qontor LabRAM spectrometer produced by Renishaw, Gloucestershire, Britain. The equivalent vitrinite reflectance value was calculated based on the equation established by Liu et al. [28]. A piece of the studied shale sample was prepared according to ISO 7404-2 [29] for solid bitumen reflectance measurement. Solid bitumen reflectance was measured using a ZEISS AX10 microscope with an integrated TIDAS CCD UV/NIR microscope photometer produced by J&M in an oil immersion objective (50× magnification) according to ASTM D7708-14 [30].
Thermal simulations were performed on the chosen shale sample using gold tubes without water. The instrument and experimental steps were described by Gai et al. [14]. The powdered samples (200–500 mg) were filled into gold tubes, which were welded at one end, and then all tubes were flooded for 25 min using argon to remove the air in them. The other end of each tube was welded under the protection of argon and placed into a stainless-steel vessel. The heating temperatures ranged from 420 °C to 610 °C (Table 1), and the confined pressures varied from 15 MPa to 60 MPa (Table 1); two heating rates, 2 °C/h and 20 °C/h, were used, respectively. The EasyRo values (Table 1) were calculated according to the method described by Sweeny and Burnham [31]. During the simulations, the accuracy of the temperature was less than ±0.5 °C, and the accuracy of the pressure was ±1 MPa. When each heating experiment was finished, the stainless-steel vessel was removed from the oven and quickly cooled by quenching it in water at room temperature.
The cooled tubes were pierced using a needle in the high vacuum line at room temperature, and the gaseous products were collected. The generated gases were then pumped into a calibrated volume to measure their volume. The gases were finally injected into a 7890A gas chromatograph, manufactured by Agilent Technologies, Inc., California, USA, for a composition analysis according to the method described by Gai et al. [14]. The stable carbon isotopic compositions of the produced gas were analyzed using a VG Isochrom II isotope ratio mass spectrometer according to the methods described by Wang et al. [32]. The solid residuals from each thermal simulation were used to measure Raman spectra using the method given above, and the equivalent vitrinite reflectance values were also calculated using the equation established by Liu et al. [28].

3. Results

3.1. Geochemical and Mineralogical Information of the Starting Sample

The geochemical parameters of the starting sample are shown in Table 2. The TOC content is 4.17%; The Tmax is 472 °C; and S1, S2, and HI are 0.36 mg/g, 2.71 mg/g, and 64.99 mg/g TOC, respectively. The equivalent vitrinite reflectance calculated from Raman spectra data according to the equation given by Liu et al. [28] is 1.26%, and that calculated from the reflectance of solid bitumen is 1.49% (Table 2 and Figure 4), suggesting that the starting sample has a relatively high maturity.
The mineralogical composition of the starting sample is listed in Table 3. The sample is mostly composed of quartz, with a content of 37.3%. The mixed layer of illite and smectite accounts for 31.1%, followed by feldspar (11.4%), pyrite (8.3%), and dolomite (6.8%).

3.2. Gas Yield

The amounts of gaseous hydrocarbons (including methane, ethane, propane, butane, and pentane) showed a variation of three stages (Figure 5). In the thermal simulations with a heating rate of 20 °C/h, the amounts first increased from 38.82 mL/g TOC to 407.13 mL/g TOC when the EqVRo values were between 1.3% and 3.07% (Figure 5 and Table 4), and then they started to decrease. At a heating rate of 2 °C/h, the generated hydrocarbons first increased from 40.5 mL/g TOC to 351.11 mL/g TOC in the EqVRo range of 1.47–3%, and then they remained stable in the EqVRo range of 3–3.25% (Figure 5) and finally decreased when the EqVRo exceeded 3.25% (Figure 5). The difference between the two heating rates in terms of the variations in the generated hydrocarbons may be attributed to the fact that the gaseous products had a longer time to achieve balance in the experiments with a slower heating rate.

3.3. Gas Compositions

The variation in the generated methane content is similar to that in the gaseous hydrocarbons, and it can also be divided into three stages (Figure 6a, Table 4). The heavier gaseous hydrocarbons (ethane (C2), propane (C3), butane (C4), and pentane (C5)) have three stages of change (Figure 6b and Table 3), including a quick increase (EqVRo 1.5–2.0%), a rapid decrease (EqVRo 2.0–2.6%), and a slow decrease (EqVRo > 2.6%). The line of the heating rate of 2 °C/h is a little lower than that of 20 °C/h, which may be related to the reason given above.

3.4. Carbon Isotopic Composition

During the thermal simulations, the stable carbon isotope values of ethane (δ13C2H6) were heavier than those of methane (δ13CH4), and the corresponding values of propane (δ13C3H8) were larger than those of δ13C2H6, all of which increased in this process (Figure 7 and Table 5). It should be noted that the (δ13CH4) values for the heating rate of 20 °C/h decreased at the EqVRo of 3%, and there was also a small decrease in the line of 2 °C/h at the EqVRo of 3.34%, which may be related to the decrease in the amounts of methane (Figure 6a), as the bond between 12C and 12C is more easily ruptured than that between two 13C atoms.

4. Discussion

4.1. Origin of Gas

Most of the gases from the shale gas reservoirs (Weiyuan, Changning, and Jiaoshiba shale gas fields) in the Longmaxi shale have a mixed-origin character with negative carbon isotopes, according to the plot established by Dai [34] (Figure 8). However, the gas samples from the thermal simulations in this study were in the areas of I, II, and IV (Figure 8), indicating their complex origins. In this study, because the starting sample was one marine shale, it mainly contained type II organic matter; additionally, as the gases produced by type II organic matter cannot be coal-derived gases, the gases obtained from the simulations should be oil-type ones. The plot of Ln (C1/C2)-Ln (C2/C3) showed that the gases from natural shale gas fields were generated by oil-cracking (Figure 9), while most of the gases obtained from the thermal simulations in this study were produced by kerogen-cracking, which was caused by the relatively high maturity of the starting sample, in which the amount of residual liquid hydrocarbons was limited (Table 3).

4.2. Gas Generation Model

As the starting sample used in the thermal simulations had an equivalent reflectance (EqVRo) of 1.26% (Table 3), the gas generated in the lower mature stage could not be quantified from the thermal simulation results. Therefore, the amount of gas produced when the EqVRo value was lower than 1.26% needed to be compensated. Ma et al. [37] conducted a set of thermal simulations using a lowly mature shale sample (0.56%EqVRo) from the Lower Silurian in the western part of Lithuania to obtain its gas generation line (Figure 10). The Lower Silurian shale that they used is considered to be the most similar to the Longmaxi shale because they have a similar sedimentary time and environment [23]. The paleogeographic location of the Lithuania shale relative to southern China is the mid-Silurian; it contains alga type II kerogen, and the macerals include graptolites, algae, and plankton bacteria [38]. In general, it is similar to the Longmaxi shale in terms of lithology, kerogen type, depositional age, and sedimentary environment. Consequently, we took the amount of gas generated from the Lithuania sample when the EasyRo was lower than 1.26% to compensate the gas generated in the same mature range for the studied sample, and a gas generation model of the Longmaxi shale was established (Figure 11).
It should be noted that the EqVRo values were generally lower than EasyRo ones (Table 1 and Table 4); because the EasyRo model was established based on the vitrinite reflectance data from coal samples [31], we believe that EqVRo can give more credible mature information. Therefore, the EqVRo values were used in this study. The gas generation process of the Longmaxi shale can be divided into three stages (Figure 11): (1) At EqVRo 0.5–3.0%, the amount of gas generated increased rapidly, the gas generation speed was stable, and a total of 458 mL/g TOC gases was produced at the end of this stage. Both kerogen-cracking gases and liquid hydrocarbon-cracking gases contributed to the gas generation in this stage. (2) At the EqVRo range of 3.0–3.25%, the amount of gas generated had no obvious changes, indicating that gas generation potentials were depleted. (3) At EqVRo > 3.25%, the amount of gas generated decreased quickly, and it dropped to 393 mL/g TOC at the EqVRo of 3.3% (Figure 11), which was caused by the onset of methane decomposition [39,40].
Our results show that the gas generation of Longmaxi shale ended at the EqVRo of 3.25% (Figure 11), which is close to the maturity limit of 3.0%Ro published by Horsfield et al. [23], who proposed this limit after comparing the chemical structures of kerogen and the results of thermal simulations of many marine shales. However, the maximum amount of gas generated from Longmaxi shale was 458 mL/g TOC, which is higher than the 335 mL/g TOC of Xiamaling shale [41] but lower than the 480 mL/g TOC of the Lower Silurian shale in the Western part of Lithuania [37]. Therefore, the gas generation model established in this study allows us to precisely determine the amount of gas generated from the Longmaxi shale in the Sichuan Basin.

4.3. Application

To test the gas generation model established in this study, we applied it to sub-layer 1 of layer 1 of Section 1 of the Longmaxi (Long11) shale in the Luzhou area of the southern Sichuan Basin (Figure 1). The shale thickness of sub-layer 1 of Long 11 was in the range of 1–2.5 m, and the shale thickness in the Nanxi–Changning area was relatively large (Figure 12). The contour map of the TOC content was obtained using the date from the wells shown in Figure 13. The TOC contents of the Longmaxi shales in the Luzhou area were generally greater than 4%, with most of them having values between 5% and 6%, and the shales in the Luxian–Nanxi area had relatively high TOC contents (Figure 13). The contour map of the equivalent vitrinite reflectance was drawn according to the reflectance values from the wells shown in Figure 14, and the relationship between EqVRo and depth was also referenced. The equivalent vitrinite reflectance values of the shales in the Luzhou area were mostly in the range of 3.3–3.5%, the shales in the middle of the Luzhou area had higher maturities, and the equivalent vitrinite reflectance values decreased around the middle area (Figure 14). The Longmaxi shale in the Luzhou area was deposited in deep water shelf environment, and the organic matter and mineral compositions showed no obvious changes. Therefore, we regarded the Longmaxi shale in the Luzhou area as a whole to estimate its amount of gas generated.
Based on the contour maps above (Figure 12, Figure 13 and Figure 14) and the gas generation model (Figure 11), we used the equation below to calculate the amount of gas generated from sub-layer 1 of Long11 in the Luzhou area, as well as the gas generation intensity.
E = (H × D × C × Q)/100
where E represents the gas generation intensity (108 m3/km2); H is the shale thickness (m); D is the shale density (t/m3), with 2.548 t/m3 used in this calculation; C is the shale organic matter content, which was calculated by dividing the TOC by 100 (TOC/100); and Q is the gas yield (mL/g TOC).
The results show that the gas generation intensity in sub-layer 1 of Long11 in the Luzhou area ranged from 0.4 × 108 m3/km2 to 1.6 × 108 m3/km2, and the shales in the Nanxi–Changning area had relatively high gas generation intensities (Figure 15). The total amount of gas generated in sub-layer 1 of Long11 in the Luzhou area was about 1.0 × 1012 m3. Previous researchers used the gas generation model of a Devonian shale sample from the Luquan area of Yunnan Province, southwestern China, to calculate the amount of gas generation from the Longmaxi shale [42]. We also computed the gas generation of sub-layer 1 of Long11 in the Luzhou area, and the results show that the gas generation intensities were between 0.2 × 108 m3/km2 and 0.6 × 108 m3/km2 (Figure 16). The total amount of gas generated was 0.4 × 1012 m3, which is much lower than the value obtained using the model proposed in this study, suggesting that the gas generation capacity of shales was seriously underestimated.
The contour map of gas content was drawn according to the gas contents from the wells shown in Figure 17, and the relationship between gas content and TOC content was also referenced. The gas contents of the shales in the Luzhou area were mostly between 11 m3/t and 17 m3/t; the shales in the southwestern part of the Luzhou area had higher gas contents (Figure 17). We used the equation below to calculate the amount of gas stored in sub-layer 1 of Long11 in the Luzhou area.
T = (S × H × D × G)/100
where T represents the total gas stored in shales (108 m3); S is the area of Luzhou area in Figure 17 (km2); H is the shale thickness (m); D is the shale density (t/m3), with 2.548 t/m3 used in this calculation; and G is the gas content (m3/t).
The results show that the gas stored in sub-layer 1 of Long11 in the Luzhou area was about 0.62 × 1012 m3, which is between 0.4 × 1012 m3 and 1.0 × 1012 m3, indicating the generated gas calculated according the gas generation model in this study is more accurate. The gas stored in shales being lower than that generated from shales could be attributed to gas migration and loss.

5. Conclusions

We conducted a series of thermal simulations on a Longmaxi shale sample of relatively low maturity using gold tubes. Then, we established a gas generation model of the Longmaxi shale, and we applied it to sub-layer 1 of Long11 in the Luzhou area. Based on these, the following conclusions could be drawn.
(1)
The gases generated in our thermal simulations were mainly oil-type gases and kerogen-cracking gases.
(2)
The gas generation process of the Longmaxi shale included three stages: (a) the gases were generated quickly in the equivalent vitrinite reflectance (EqVRo) range of 0.5–3.0%; (b) the amount of gas generated was stable in the EqVRo range of 3.0–3.25%; and (c) the amount of gas generated decreased when EqVRo exceeded 3.25%.
(3)
The calculation results show that the total amount of gas generated in sub-layer 1 of Long11 in the Luzhou area was about 1.0 × 1012 m3, and the gas generation intensities were in the range of 0.4 × 108 m3/km2–1.6 × 108 m3/km2. In a comparison with the results obtained using other gas generation models, it was found that the accuracy of the gas generation estimation of the Longmaxi shale significantly improved.

Author Contributions

Conceptualization, Y.J.; methodology, Y.J.; software, Z.W.; investigation, Y.F. and Y.G.; resources, X.S., Y.L., Y.Z., W.W. and Z.Z.; writing—original draft preparation, X.S.; writing—review and editing, Y.J.; formal analysis, X.Y.; visualization, X.Y.; supervision, Y.J. and Z.W.; funding acquisition, Y.J. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by PetroChina grant number [2023ZZ21YJ04], Shale Gas Research Institute, PetroChina Southwest Oil and Gas Field Company grant number [JS2022-06], and the Natural Science Foundation of Sichuan Province grant number [2024NSFSC0088].

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding authors.

Conflicts of Interest

Xuewen Shi, Yi Li and Wei Wu were employed by Shale Gas Research Institute, PetroChina Southwest Oil & Gasfield Company. The remaining authors declare that the research was con-ducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Figure 1. Locations of Well Yucan 1 and the Luzhou area (modified from Wang et al. [16]).
Figure 1. Locations of Well Yucan 1 and the Luzhou area (modified from Wang et al. [16]).
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Figure 2. Stratigraphic framework and sampling location of Well Yucan 1 (modified from Zhang et al. [26]). HST: highstand system tract; TST: transgressive system tract; SSQ1: second-order sequence 1; SSQ2: second-order sequence 2; SSQ3: second-order sequence 3; GR: the well log parameter of natural gamma rays, API; AC: the well log parameter of acoustics, µs/ft.
Figure 2. Stratigraphic framework and sampling location of Well Yucan 1 (modified from Zhang et al. [26]). HST: highstand system tract; TST: transgressive system tract; SSQ1: second-order sequence 1; SSQ2: second-order sequence 2; SSQ3: second-order sequence 3; GR: the well log parameter of natural gamma rays, API; AC: the well log parameter of acoustics, µs/ft.
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Figure 3. Sample obtained from Longmaxi Formation at 1202.85 m–1202.92 m of Well Yucan 1.
Figure 3. Sample obtained from Longmaxi Formation at 1202.85 m–1202.92 m of Well Yucan 1.
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Figure 4. The equivalent vitrinite reflectance (EqVRo2) histogram of the starting sample (EqVRo2 represents the equivalent vitrinite reflectance calculated from the reflectance of solid bitumen according to the equation established by Jacob [33]).
Figure 4. The equivalent vitrinite reflectance (EqVRo2) histogram of the starting sample (EqVRo2 represents the equivalent vitrinite reflectance calculated from the reflectance of solid bitumen according to the equation established by Jacob [33]).
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Figure 5. Total gaseous hydrocarbon generation lines obtained from thermal simulations using gold tubes. ① stage 1 (1.3–3.0%, EqVRo), generated gases increased quickly; ② stage 2 (3.0–3.25%, EqVRo), generated gases were stable; ③ stage 3 (EqVRo > 3.25%), generated gases decreased.
Figure 5. Total gaseous hydrocarbon generation lines obtained from thermal simulations using gold tubes. ① stage 1 (1.3–3.0%, EqVRo), generated gases increased quickly; ② stage 2 (3.0–3.25%, EqVRo), generated gases were stable; ③ stage 3 (EqVRo > 3.25%), generated gases decreased.
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Figure 6. Gas generation lines obtained from thermal simulations using gold tubes. (a) The amount of methane (CH4) varied with the increasing EqVRo, ① stage 1 (1.3–3.0%, EqVRo), generated methane increased quickly; ② stage 2 (3.0–3.25%, EqVRo), generated methane was stable; ③ stage 3 (EqVRo > 3.25%), generated methane decreased; (b) the amount of heavier gaseous hydrocarbons (C2–C5) varied with the increasing EqVRo, ① stage 1 (1.5–2.0%, EqVRo), generated C2–C5 increased quickly; ② stage 2 (2.0–2.6%, EqVRo), generated C2–C5 decreased quickly; ③ stage 3 (EqVRo > 2.6%), generated C2–C5 decreased slowly.
Figure 6. Gas generation lines obtained from thermal simulations using gold tubes. (a) The amount of methane (CH4) varied with the increasing EqVRo, ① stage 1 (1.3–3.0%, EqVRo), generated methane increased quickly; ② stage 2 (3.0–3.25%, EqVRo), generated methane was stable; ③ stage 3 (EqVRo > 3.25%), generated methane decreased; (b) the amount of heavier gaseous hydrocarbons (C2–C5) varied with the increasing EqVRo, ① stage 1 (1.5–2.0%, EqVRo), generated C2–C5 increased quickly; ② stage 2 (2.0–2.6%, EqVRo), generated C2–C5 decreased quickly; ③ stage 3 (EqVRo > 2.6%), generated C2–C5 decreased slowly.
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Figure 7. Stable carbon isotope values of methane and ethane obtained from thermal simulations using gold tubes.
Figure 7. Stable carbon isotope values of methane and ethane obtained from thermal simulations using gold tubes.
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Figure 8. The determination of gas origin of shale gas from the Longmaxi Formation (chart according to Dai [34]) (I. Coal-derived gas, II. Oil-type gas, III. Mixed gas with negative carbon isotopes, IV. Coal-derived gas or oil-type gas, V. Coal-derived gas, oil-type gas, or mixed gas, and VI. Biogenic gas. The data of Weiyuan, Changning, and Jiaoshiba shale gas fields were after Dai et al. [35]).
Figure 8. The determination of gas origin of shale gas from the Longmaxi Formation (chart according to Dai [34]) (I. Coal-derived gas, II. Oil-type gas, III. Mixed gas with negative carbon isotopes, IV. Coal-derived gas or oil-type gas, V. Coal-derived gas, oil-type gas, or mixed gas, and VI. Biogenic gas. The data of Weiyuan, Changning, and Jiaoshiba shale gas fields were after Dai et al. [35]).
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Figure 9. Identification of kerogen-cracking gas and oil-cracking gas using plot of Ln (C1/C2)-Ln (C2/C3) (chart according to Li et al. [36]). The data of Weiyuan, Changning, and Jiaoshiba shale gas fields were after Dai et al. [35]).
Figure 9. Identification of kerogen-cracking gas and oil-cracking gas using plot of Ln (C1/C2)-Ln (C2/C3) (chart according to Li et al. [36]). The data of Weiyuan, Changning, and Jiaoshiba shale gas fields were after Dai et al. [35]).
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Figure 10. Gas generation of the Lower Silurian black shale from eastern Lithuania (based on the study by Ma et al. [37]).
Figure 10. Gas generation of the Lower Silurian black shale from eastern Lithuania (based on the study by Ma et al. [37]).
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Figure 11. Gas generation model of the Longmaxi shale. ① stage 1 (0.5–3.0%, EqVRo), generated gases increased quickly; ② stage 2 (3.0–3.25%, EqVRo), generated gases were stable; ③ stage 3 (EqVRo > 3.25%), generated gases decreased quickly.
Figure 11. Gas generation model of the Longmaxi shale. ① stage 1 (0.5–3.0%, EqVRo), generated gases increased quickly; ② stage 2 (3.0–3.25%, EqVRo), generated gases were stable; ③ stage 3 (EqVRo > 3.25%), generated gases decreased quickly.
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Figure 12. Contour map of shale thickness in sub-layer 1 of Long11 in the Luzhou area.
Figure 12. Contour map of shale thickness in sub-layer 1 of Long11 in the Luzhou area.
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Figure 13. Contour map of the TOC content in sub-layer 1 of Long11 in the Luzhou area.
Figure 13. Contour map of the TOC content in sub-layer 1 of Long11 in the Luzhou area.
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Figure 14. Contour map of EqVRo in sub-layer 1 of Long11 in the Luzhou area.
Figure 14. Contour map of EqVRo in sub-layer 1 of Long11 in the Luzhou area.
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Figure 15. Contour map of gas generation intensity from sub-layer 1 of Long11 in the Luzhou area (calculated according to the gas generation model proposed in this study).
Figure 15. Contour map of gas generation intensity from sub-layer 1 of Long11 in the Luzhou area (calculated according to the gas generation model proposed in this study).
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Figure 16. Contour map of gas generation intensity from sub-layer 1 of Long11 in the Luzhou area (calculated according to the gas generation model in the study by Guo et al. [42]).
Figure 16. Contour map of gas generation intensity from sub-layer 1 of Long11 in the Luzhou area (calculated according to the gas generation model in the study by Guo et al. [42]).
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Figure 17. Contour map of gas content from sub-layer 1 of Long11 in the Luzhou area.
Figure 17. Contour map of gas content from sub-layer 1 of Long11 in the Luzhou area.
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Table 1. Experimental scheme of the thermal simulations using gold tubes.
Table 1. Experimental scheme of the thermal simulations using gold tubes.
Heating Rate
(°C/h)		Confined Pressure
(MPa)		Final Temperature
(°C)		EasyRo
(%)		Time at Final Temperature
(h)
20154601.500
204902.000
255202.500
305503.020
355753.480
406094.010
456104.5312
506104.6824
556104.6948
2154201.520
204501.980
254802.300
305002.910
355303.490
355603.980
406004.530
506004.6312
606004.6848
Table 2. Geochemical parameters of the starting sample.
Table 2. Geochemical parameters of the starting sample.
TOC
(%)		Tmax
(°C)		S1
(mg/g)		S2
(mg/g)		S3
(mg/g)		HI
(mg/g TOC)		OI
(mg/g TOC)		* EqVRo1
(%)		** EqVRo2
(%)
4.174720.362.710.3264.997.671.261.49
* EqVRo1 represents the equivalent vitrinite reflectance calculated from Raman spectra data according to the equation given by Liu et al. [28]. ** EqVRo2 represents the equivalent vitrinite reflectance calculated from the reflectance of solid bitumen according to the equation established by Jacob [33].
Table 3. Mineral composition of the starting sample.
Table 3. Mineral composition of the starting sample.
Quartz
(%)		Feldspar
(%)		Dolomite
(%)		Pyrite
(%)		Kaolinite
(%)		Chlorite
(%)		Illite
(%)		** I/S
(%)		*** C/S
(%)
37.311.46.88.30.351.332.1231.11.3
** I/S represents the mixed layer of illite and smectite. *** C/S represents the mixed layer of chlorite and smectite.
Table 4. Generated gaseous hydrocarbons obtained from thermal simulations using gold tubes.
Table 4. Generated gaseous hydrocarbons obtained from thermal simulations using gold tubes.
Heating Rate
(°C/h)		Final Temperature
(°C)		EqVRo (%)Generated Gas (mL/g TOC)
Methane
(C1)		Ethane
(C2)		Propane (C3)Butane
(C4)		Pentane
(C5)		C2–5C1–5
204601.328.457.702.260.310.0210.3738.82
4901.973.8912.062.030.150.0014.2788.16
5202.32136.5210.880.610.010.0011.51148.03
5502.58196.015.600.050.000.005.66201.66
5752.83226.872.260.010.000.002.27229.14
6092.93267.091.430.000.000.001.43268.52
6103370.040.420.000.000.000.42370.46
6103.07407.130.000.000.000.000.00407.13
6103.16348.470.330.000.000.000.33348.80
24201.4729.858.062.240.310.0310.6540.50
4501.8984.3712.851.850.140.0014.8599.22
4802.15157.219.560.300.010.009.87167.07
5002.58201.134.790.050.000.004.84205.97
5302.65242.421.560.020.000.001.58244.00
5602.76265.020.920.010.000.000.93265.95
6003350.770.340.000.000.000.34351.11
6003.25347.710.340.000.000.000.34348.05
6003.34286.050.290.000.000.000.29286.34
Table 5. Stable carbon isotope values of the generated gaseous hydrocarbons obtained from thermal simulations using gold tubes.
Table 5. Stable carbon isotope values of the generated gaseous hydrocarbons obtained from thermal simulations using gold tubes.
Heating Rate
(°C/h)		Final Temperature
(°C)		EqVRo (%)Stable Carbon Isotope Values (‰, PDB)
Methane
(δ13C1)		Ethane
(δ13C2)		Propane (δ13C3)
204601.3−38.43−31.43−28.45
4901.9−34.93−28.7−25.37
5202.32−32.01−19.43
5502.58−30.46−12.37
5752.83−30.43
6092.93−30.63
6103−31.1
6103.07−33.69
6103.16−32.97
24201.47−37.91−31.68−29.5
4501.89−34.57−28.29−25.36
4802.15−32.7−17.3
5002.58−32.32
5302.65−31.59
5602.76−31.48
6003−31.68
6003.25−30
6003.34−30.21
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Shi, X.; Li, Y.; Jiang, Y.; Zhang, Y.; Wu, W.; Zhang, Z.; Wang, Z.; Yin, X.; Fu, Y.; Gu, Y. A Model and the Characteristics of Gas Generation of the Longmaxi Shale in the Sichuan Basin. Processes 2025, 13, 2294. https://doi.org/10.3390/pr13072294

AMA Style

Shi X, Li Y, Jiang Y, Zhang Y, Wu W, Zhang Z, Wang Z, Yin X, Fu Y, Gu Y. A Model and the Characteristics of Gas Generation of the Longmaxi Shale in the Sichuan Basin. Processes. 2025; 13(7):2294. https://doi.org/10.3390/pr13072294

Chicago/Turabian Style

Shi, Xuewen, Yi Li, Yuqiang Jiang, Ye Zhang, Wei Wu, Zhiping Zhang, Zhanlei Wang, Xingping Yin, Yonghong Fu, and Yifan Gu. 2025. "A Model and the Characteristics of Gas Generation of the Longmaxi Shale in the Sichuan Basin" Processes 13, no. 7: 2294. https://doi.org/10.3390/pr13072294

APA Style

Shi, X., Li, Y., Jiang, Y., Zhang, Y., Wu, W., Zhang, Z., Wang, Z., Yin, X., Fu, Y., & Gu, Y. (2025). A Model and the Characteristics of Gas Generation of the Longmaxi Shale in the Sichuan Basin. Processes, 13(7), 2294. https://doi.org/10.3390/pr13072294

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