Sandstone reservoirs exhibit strong heterogeneity and complex microscopic pore structures, presenting challenges for quantitative characterization. This study investigates the Chang 8 tight sandstone reservoir in the Jiyuan, Ordos Basin through analyses of its physical properties, high-pressure mercury injection (HPMI), casting thin sections (CTS), and scanning electron microscopy (SEM). Deep learning techniques were employed to extract the geometric parameters of the pores from the SEM images. Fractal geometry was applied for the combined quantitative characterization of pore parameters and fractal dimensions of the tight sandstone. This study also analyzed the correlations between the fractal dimensions, sample properties, pore structure, geometric parameters, and mineral content. The results indicate that the HPMI-derived fractal dimension (D_MIP) reflects pore connectivity and permeability. D_MIP gradually increases from Type I to Type III reservoirs, indicating deteriorating pore connectivity and increasing reservoir heterogeneity. The average fractal dimensions of the small and large pore-throats are 2.16 and 2.52, respectively, indicating greater complexity in the large pore-throat structures. The SEM-derived fractal dimension (D_SEM) reflects the diversity of pore shapes and the complexity of the micro-scale geometries. As the reservoir quality decreases, the pore structure becomes more complex, and the pore morphology exhibits increased irregularity. D_MIP and D_SEM values range from 2.21 to 2.49 and 1.01 to 1.28, respectively, providing a comprehensive quantitative characterization of multiple pore structure characteristics. The fractal dimension shows negative correlations with permeability, porosity, median radius, maximum mercury intrusion saturation, mercury withdrawal efficiency, and sorting factor, while showing a positive correlation with median and displacement pressures. Among these factors, the correlations with the maximum mercury intrusion saturation and sorting factor are the strongest (R² > 0.8). Additionally, the fractal dimension is negatively correlated with pore circularity and major axis length, but positively correlated with pore perimeter, aspect ratio, and solidity. A higher proportion of circular pores and fewer irregular or long-strip pores correspond to lower fractal dimensions. Furthermore, mineral composition influences the fractal dimension, showing negative correlations with feldspar, quartz, and chlorite concentrations, and a positive correlation with carbonate content. This study provides new perspectives for the quantitative characterization of pore structures in tight sandstone reservoirs, enhances the understanding of low-permeability formation reservoir performance, and establishes a theoretical foundation for reservoir evaluation and exploration development in the study area.

Marine–continental transitional (hereinafter referred to as transitional) Permian shales are important targets for shale gas in China because of the considerable volumes of shale gas resources present in them. In this study, transitional shale samples from the Permian Shanxi Formation in the Daning–Jixian block along the eastern margin of the Ordos Basin were collected to investigate the effects of organic and inorganic compositions on the development of their pore structures through organic petrographic analysis, X–ray diffraction, scanning electron microscope (SEM) observation, gas (N₂ and CO₂) adsorption, high-pressure mercury injection (HPMI), and methane adsorption experiments. The organic petrographic analysis reveals that the Permian Shanxi shale comprises Type-II₂-III kerogens, and the average vitrinite reflectance (R_o) is 2.3% at the overmature stage or in the dry gas window. The shale interval at the bottom of the lagoon facies is considered the most favorable interval throughout the entire section because of its high total organic carbon (TOC) content (4.19–43.9%; an average of 16.9%) and high brittle mineral content (38.3–73.2%; an average of 55.8%). N₂ and CO₂ gas adsorption and HPMI tests reveal the pore size distribution characteristics of the shale. The full pore size distribution by the gas adsorption and HPMI test reveals that micropores (<2 nm) and mesopores (2–50 nm) were dominant in the pore system, and the contributions of the two pore sizes were nearly equivalent, accounting for 21.95–55.05% (an average of 42.3%) and 37.94–64.6% (an average of 49.64%) of the total pore volume, respectively. Additionally, the pore characteristics related to different phases (mainly as silicate, clays, and organic matter) are further clarified by SEM observation and correlation analysis of phase content and pore structure parameters. OM contains numerous SEM-invisible micropores, whereas clay minerals mainly develop mesopores and small macropores (50–100 nm). Furthermore, we calculated the contribution of different shale components to shale porosity. The OM pores account for 0.26–44.1% (an average of 18.7%), and clay mineral pores account for 53.8–93.3% (an average of 76.9%) of the shale porosity. In particular, the OM contributes 73.2% to the surface area and 33.5% to the pore volume. This implies that both OM and clay minerals are important for the storage capacity of adsorbed and free gas.

In the Carboniferous–Permian period, several organic-rich black shales were deposited in a marine–continental transitional environment in the Linfen area on the eastern margin of the Ordos Basin. Integrated sedimentological and organic geochemical analyses are performed on an outcrop in order to clarify the relationship between paleoenvironment and organic matter accumulation. The results of this study show that the marine–continental transitional strata of the Upper Carboniferous Benxi Formation to Lower Permian Taiyuan and Shanxi Formation exposed in the Linfen area are composed of sandstone, shale, coal, and limestone. Total organic carbon (TOC) contents of the studied samples were mainly distributed in the range of 0.59%–35.4%, with an average of 7.32%. From Benxi Formation to Shanxi formation, the humidity gradually increased, and the climate gradually changed from hot and humid to warm and humid during Carboniferous to Permian. The deposition of the Shanxi Formation ended with the climate returning to hot and humid, having an oxic-suboxic conditions and a high paleoproductivity. Paleoredox conditions and paleoproductivity are the two vital factors controlling the formation of organic matter in black shales. The transitional environment characterized by oxic-suboxic, relatively high deposition rate, and various source of organic matter, although different from the marine environment, provides a good material basis for the deposition of organic-rich shales.

Transitional shale gas, rich in resources, is expected to be a practical contributor to the increase in shale gas reserves and production in China. Its exploration prospect has been demonstrated by several wells in the Daning-Jixian block on the eastern margin of the Ordos Basin. In this paper, the Lower Permian Shanxi Formation (P₁s) and Taiyuan Formation (P₁t) in the eastern margin of Ordos Basin were compared for organic geochemical parameters, revealing that the overflow fan + lagoon combination (OLC) of the third sub-member of the second member of Shanxi Formation (P₁s₂³) and the marine + lagoon combination (MLC) of the first member of Taiyuan Formation (P₁t₁) are the most favorable shale gas intervals. The two intervals were comparatively analyzed with respect to mineral composition, brittleness, caprocks, and preservation conditions. It is found that the OLC of P₁s₂³ has a similar porosity to and much lower permeability than the MLC of P₁t₁ (or MLC1) and a BET surface area of 10–15 m²/g, which is smaller than the MLC1 (15–20 m²/g). Moreover, OLC has a brittle mineral content equivalent to MLC1 but a brittleness index of 33.73–62.36 (avg. 49.86), smaller than MLC1 (53.34–58.27, or avg. 55.85). OLC contains sandstones at both the roof and floor, with a higher permeability than shale in the interval, which cannot serve as good physical seals. In contrast, MLC1 contains limestones with lower permeability at the roof and floor, which, together with the overlying coal seams, have hydrocarbon generation capacity and can physically seal the MLC1 shale but also fill it with hydrocarbons, making MLC1 have higher gas content and superior for shale gas exploration than the OLC shale. Due to the multi-lithologies developed in transitional facies, besides the organic matter enrichment, and reservoir characteristics, it is necessary to find a suitable lithological combination to ensure the gas in shale can be better preserved and retained.

Natural gas, as a sustainable and cleaner energy source, still holds a crucial position in the energy transition stage. In shale gas exploration, total organic carbon (TOC) content plays a crucial role, with log data proving beneficial in predicting total organic carbon content in shale reservoirs. However, in complex coal-bearing layers like the marine–continental transitional Shanxi Formation, traditional prediction methods exhibit significant errors. Therefore, this study proposes an advanced, cost- and time-saving deep learning approach to predict TOC in marine–continental transitional shale. Five well log records from the study area were used to evaluate five machine learning models: K-Nearest Neighbors (KNNs), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Extreme Gradient Boosting (XGB), and Deep Neural Network (DNN). The predictive results were compared with conventional methods for accurate TOC predictions. Through K-fold cross-validation, the ML models showed superior accuracy over traditional models, with the DNN model displaying the lowest root mean square error (RMSE) and mean absolute error (MAE). To enhance prediction accuracy, δR was integrated as a new parameter into the ML models. Comparative analysis revealed that the improved DNN-R model reduced MAE and RMSE by 57.1% and 70.6%, respectively, on the training set, and by 59.5% and 72.5%, respectively, on the test set, compared to the original DNN model. The Williams plot and permutation importance confirmed the reliability and effectiveness of the enhanced DNN-R model. The results indicate the potential of machine learning technology as a valuable tool for predicting crucial parameters, especially in marine–continental transitional shale reservoirs lacking sufficient core samples and relying solely on basic well-logging data, signifying its importance for effective shale gas assessment and development.