Fig 1.
Location and the lithology column of the investigated well in Southern Songliao Basin, China (adapted from Ref.
[26]). During the sedimentary epoch of the Qingshankou Formation, extensive aquatic incursions led to the deposition of copious layers of organic-rich dark mudstone and shale. The Qingshankou Formation not only serves as a source rock for conventional hydrocarbons but also emerges as a target for shale oil exploration endeavors [9, 27].
Fig 2.
The Tmax values fluctuate between 435°C and 454°C, with an average of 447°C. This suggests that the lacustrine shale samples have reached a mature phase in their thermal evolutionary trajectory. The hydrogen index, denoted as HI (HI = S2/TOC), spans from 334 mg/g to 718 mg/g, with a mean of 505 mg/g. The organic matter typology can be ascertained through a Tmax versus HI plot [31]. As illustrated in Fig 3, the kerogen classifications for the Qingshankou Formation’s lacustrine shales predominantly align with Type I. This underscores the exceptional oil generation potential of the lacustrine shales in the study area.
Fig 3.
Plot of Tmax versus HI.
Table 1.
Organic geochemistry of the K2qn1 lacustrine shale samples.
Fig 4.
2D NMR spectrums showing 1H compounds in shale sample D86-31, 1993.9 m.
Table 2 delineates the data: The content of solid organic matter ranged from 0.19–3.39 μl/g, averaging at 1.81 μl/g. The concentration of light oil spanned between 1.15–6.33 μl/g, with a mean value of 3.10 μl/g. The hydroxyl content varied from 13.96–28.92 μl/g, settling at an average of 21.93 μl/g, while the water content within pore systems fluctuated from 2.10–13.39 μl/g, with an average content of 7.21 μl/g.
Table 2.
Fluid contents of different phases derived from 2D-NMR measurement.
Fig 5.
Relationships between geochemistry parameters and 2D-NMR parameters.
A: Plot of 2D-NMR light oil content and S1; B: Plot of 2D-NMR solid OM content and TOC.
Fig 6.
Mineral compositions of the K2qn1 lacustrine shale samples.
Table 3.
Mineralogical compositions of the K2qn1 lacustrine shale samples.
Fig 7.
Classification of shale lithofacies of the K2qn1 lacustrine shale samples.
Fig 8.
N2 adsorption and desorption isotherms of the K2qn1 lacustrine shale samples.
The BET model can be applied to the SSA and average pore diameter (APD) of multi-scale pore media, while the premise assumption of the Kelvin’s equation is based on a single pore size. Therefore, for shale, a porous medium with a wide range of pore sizes, the effective model for obtaining SSA and APD should use the BET model, which is also widely used in the industry. For computational purposes, the BET model was utilized for the estimation of SSA and APD (BET-SSA and BET-APD), and the BJH model was employed to determine PV. As presented in Table 4, BJH-PV values oscillate between 0.0201 cm3/g and 0.0378 cm3/g, with a mean of 0.0256 cm3/g. BET-SSA values span from 5.47 m2/g to 20.60 m2/g, averaging at 10.17 m2/g. Concurrently, BET-APD values range from 5.60 nm to 9.79 nm, averaging at 7.81 nm.
Table 4.
LTNA pore structure parameters and fractal dimensions of the K2qn1 lacustrine shale samples.
Fig 9.
Fractal dimension calculation results with ln(V) versus ln(ln(P/P0)) from N2 adsorption isotherms.
D1 fluctuates between 2.5715 and 2.7551, with the average of 2.6564; while D2 ranged from 2.3247 to 2.4209, with the average of 2.3653. Notably, D1 consistently surpasses D2, suggesting that smaller pores exhibit greater homogeneity compared to their larger counterparts (refer to Table 4).
Fig 10.
Relationships between pore structure parameters and fractal dimensions.
Notably, the correlation coefficient R2 of D1 with BJH-PV, BET-SSA, and BET-APD is markedly superior to that of D2. This underscores the aptness of D1 in characterizing the microporous architecture of the K2qn1 lacustrine shale specimens from the designated study region.
Fig 11.
Relationships between clay and quartz content, TOC, 2D-NMR solid OM, S1, 2D-NMR light oil and fractal dimensions.
Understanding the relationship between TOC and fractal dimension D holds significant importance in gaining a deeper understanding of the pore structure of shale reservoirs and the mechanism of shale oil enrichment. The correlation between fractal dimension D and TOC is depicted as negative in Fig 11C. As the TOC content escalates, the fractal dimensions diminish. Both D1 and D2 exhibit a robust inverse relationship with TOC, boasting correlation coefficients (R2) of 0.8205 and 0.7799, respectively. This phenomenon is similar to the data of lacustrine shale from Qingshankou Formation documented by Wang and Lacustrine Oil-Bearing Shale from the Dongying Sag documented by Zhang when the TOC content is less than 3%, but contrasts that of the aforementioned gas marine shale [7, 37, 42]. The reasons for this phenomenon can be attributed to several factors. In shale within the oil window, the organic matter contains relatively few pores, which means that the pore structure and surface remain largely unaffected by the organic matter itself. Consequently, the fractal dimensions do not increase with rising total organic carbon (TOC) content. Furthermore, as the organic matter content (TOC) increases, a greater quantity of organic acids is generated during the hydrocarbon generation process. This results in the formation of larger dissolution pores within the carbonated minerals [43], leading to a decrease in the fractal dimensions D1 and D2. There is also a negative correlation between fractal dimension D and 2D-NMR OM content (Fig 11D). This suggests that both fractal dimensions are apt for characterizing the TOC content.