The breakthrough of bauxite natural gas in the Taiyuan Formation in the Longdong area of the Ordos Basin makes bauxite natural gas gradually become a new exploration direction. However, the complex sedimentary conditions and diverse developmental strata of the Linxing region present difficulties for the exploration of bauxite gas. In this research, samples of bauxite cores from significant drilling sections in the Linxing area were obtained for comprehensive geochemical, mineralogical, and pore structure analysis. This study investigates the impact of sedimentary environments on the physical properties of bauxite reservoirs within the Benxi Formation, focusing on lithofacies categorization, mineral composition, and pore structure. This study established a lithofacies categorization system that iron minerals, aluminum minerals + titanium minerals, clay minerals as three end‐member components. The study identified four lithofacies types: pure bauxite, clayey bauxite, mixed bauxite, and bauxite mudstone. According to geochemical analysis, the bauxite rock in the study area is deposited in a semi‐enclosed bay with terrestrial freshwater input. The physical properties of bauxite reservoir in the study area are relatively poor, among which the physical properties of bauxite reservoir are the best. Authigenic minerals like pyrite are often filled in dissolution pores. The constraint of sedimentary environment on bauxite reservoir is mainly manifested as the influence on the formation of diaspore mineral framework, and its acidity and alkalinity are very important for the formation of diaspore. Hence, diaspore commonly forms in the shallow lake environments characterized by pure bauxite rock, exhibiting favorable reservoir conditions. This research examines the impact of mineral composition on bauxite reservoirs through the lens of lithofacies and sedimentary environments. The variations in bauxite rock reservoirs across distinct sedimentary environments have been elucidated. It provides a new guidance for the exploration of natural gas in Benxi Formation bauxite in Ordos Basin.

Tight sandstone gas (TSG), an unconventional oil–gas resource, has heterogeneous reservoirs. Traditional classification and evaluation methods fail to fully reveal reservoir characteristics and complex reservoir‐productivity links. As a key source of tight sandstone, the Taiyuan Formation in the Ordos Basin exhibits significant variations in reservoir quality due to its marine–terrestrial transitional sedimentary setting, necessitating the establishment of an effective classification and evaluation system. This study focuses on Block A's Taiyuan Formation tight sandstone reservoirs in the Ordos Basin, using core observations, thin‐section petrography, scanning electron microscopy, and high‐pressure mercury intrusion experiments to build a machine learning (ML) model for reservoir evaluation and improve the accuracy of reservoir evaluation. Results demonstrate that the K ‐means algorithm has the optimal classification results based on a variety of internal evaluation metrics. The tight sandstone reservoirs in the study area are classified into three distinct types: Type I reservoirs are mainly lithic quartz sandstone/lithic feldspathic sandstone, showing significant compaction, with large pore throats and low displacement pressure. Type II, mostly feldspathic lithic sandstone, acts as a transitional reservoir. Type III, predominantly lithic feldspathic sandstone, is most affected by cementation, featuring fine‐grained pore throats and high displacement pressure. Furthermore, evaluation results based on confusion matrices demonstrate that the Bayesian‐optimized Random Forest model achieves a classification accuracy of 92.76% in log interpretation, significantly outperforming other models. Through gray relational analysis, the nonlinear correlations between productivity and reservoir parameters are clarified. The prediction results demonstrate over 80% spatial consistency with actual gas reservoir distributions, effectively reflecting the production capacity of the tight sandstone reservoirs in the study area. The ML‐driven evaluation approach proposed in this study not only provides robust technical support for the exploration and development of TSG reservoirs and effectively delineates the distribution of favorable zones, but also offers practical guidance for the efficient exploration of hydrocarbon resources in geologically analogous settings.

The shale of the Lower Cambrian Shuijingtuo Formation, as a key target for shale gas exploration in the Western Hubei region, possesses favorable conditions such as great thickness, high organic matter abundance, and moderate thermal maturity. However, the unclear mechanisms governing pore development have constrained sweet spot prediction, and there is a lack of quantitative characterization of the synergistic effects of multiple factors on pore development. To address the scientific questions of how multiple factors synergistically influence pore development and how to quantitatively evaluate the reservoir performance of different lithofacies, multi-scale experimental methods, including field emission scanning electron microscopy (FE-SEM), gas adsorption, high-pressure mercury intrusion, and fractal dimension analysis, were comprehensively applied in this study. Research indicates that fractal dimension positively correlates with TOC and micropore development, while negatively correlating with clay mineral content, serving as an effective indicator for characterizing the complexity of shale pore structures. Siliceous minerals resist compaction and preserve pores through rigid framework support, whereas clay minerals inhibit pore development through compaction and cementation, with carbonate minerals having a relatively limited influence. Higher TOC content drives the expansion of pore volume and specific surface area, predominantly within the micro-mesopore range. This results in a reduction of the average pore size. The shale pore system is synergistically controlled by a ternary mechanism of “rigid mineral support–organic matter pore generation–clay mineral inhibition,” with the configuration quality of these three factors directly controlling reservoir quality. To quantify the differences in reservoir properties among different lithofacies, a semi-quantitative evaluation model centered on the “Anti-compaction Index (ACI)–Organic Pore Contribution Index (OPCI)–Cementation and Filling Intensity Index (CFI)” was constructed. The evaluation results indicate that the highly organic siliceous shale (HS facies), characterized by an optimal synergistic configuration of “high OPCI, high ACI, and low CFI”, this lithofacies represents the optimal reservoir facies within the study area. This model enables the quantitative evaluation and comparison of reservoir performance across various shale lithofacies, providing an operable geological-quantitative basis for shale gas sweet spot prediction and favorable area optimization in the Western Hubei province.