Marine–continental transitional shale is one of the most promising targets for shale gas exploration in the Lower Yangtze region. To investigate the sedimentary environments and the regularity of the enrichment of the Longtan shale, multiple techniques including core and thin-section observations, geochemical and elemental analyses, X-ray diffraction, scanning electron microscopy (SEM), and low-pressure nitrogen adsorption (LPNA) were used to analyze the sedimentology, mineralogy, and pore structure of the Longtan shale. The core descriptions and thin-section observations showed that the Longtan shale was deposited in marine–delta transitional environments including delta-front, shore swamp, mixed tidal flat and shallow shelf environments. The Sr/Cu, V/Cr, CIA, EF (Mo), EF (U), and other major and trace element results indicated warm and moist climates and water-reducing conditions in the Longtan period. Both the climate and water conditions were favorable for organic matter production and preservation. The geochemical results showed that the Longtan shale was in the overmature stage (Ro values ranging from 2.4% to 3.57%) and that the average total organic carbon (TOC) content was 5.76%. The pore system of the Longtan shale consisted of inorganic pores with a small number of organic pores and microfractures. The porosity and specific surface area were mainly affected by the TOC and clay mineral contents. An effective combination of brittle mineral particles, organic matter, and clay minerals provided the necessary conditions for pore preservation. The organic pores, intergranular pores in clay minerals, and brittle mineral pores formed the main network system for the Longtan shale. In summary, the lithological combinations, organic geochemistry, and pore structure system were all affected by the sedimentary environments.

The transitional shale system of the Longtan Formation (LTF) is widely distributed in the Sichuan Basin. However, the lithofacies of the LTF shale system exhibit vertical variations, with frequent interbedding of blocks, and shale–sand–coal sequences, which makes identifying “sweet spots” a challenging task. To address this issue, lithofacies associations were investigated based on well log analysis from 30 wells, and experimental data from 19 well samples, including X-ray diffraction, total organic carbon (TOC), pore structure characterization, and methane isothermal adsorption tests. Four lithofacies associations were classified: carbon–shale interbedding (I-1), shale(carbon)–coal interbedding (I-2), shale–sand interbedding (II), and shale–sand–coal assemblage (III). A favorable lithofacies association index (Com) was developed, providing a quantitative method for identifying favorable lithofacies. The results indicate that among the four lithofacies associations, I-2 is the most favorable lithofacies association. The Com index threshold for favorable lithofacies is defined as 0.6, and for the most favorable lithofacies, it is 0.7. Overall, favorable lithofacies are primarily distributed in the Suining-Dazu and Lujiao areas.

Thin layers and high-yield dolomite reservoirs were recently discovered in the Permian Guadeloupian Maokou Formation. The genetic mechanism of this reservoir is controversial because of its complex sedimentation and diagenesis in the Maokou Formation. Traditionally, the genesis has focused on sedimentation, karst, and fracture, whereas the influence of the Emeishan mantle plume activity (EMP) has been ignored. In this study, we enumerated petrographic (grouped into micritic bioclastic limestone, limy dolomite, grain dolomite, dolomite cement, calcite cement, and saddle dolomite) and geochemical data (δ¹³C, δ¹⁸O, REE, and ⁸⁷Sr/⁸⁶Sr) from a microscopic perspective to support the impact of EMP on reservoirs. We conclude that EMP activity altered the sedimentary environment and induced a complex diagenesis. The paleogeomorphic reconstruction data indicate that the EMP caused an uplift zone in the NE–SW direction, depositing advantageous high-energy beach facies. In terms of diagenesis, the abnormally high ⁸⁷Sr/⁸⁶Sr ratios and REE with positive Eu anomalies suggest that dolomitization was influenced by both seawater and hydrothermal fluids. Based on the above evidence, we established a reservoir genetic model for the Maokou Formation related to the intensity of the EMP. This study provides a new perspective on the mantle plume activity for the study of carbonate reservoir genesis.

To address the limitations of traditional methods in modeling complex nonlinear relationships in horizontal in situ stress prediction for shale reservoirs, this study proposes an integrated framework that combines well logging interpretation with machine learning to accurately predict horizontal in situ stress in shale reservoirs. Based on the logging data from five wells in the Luzhou Block of the Sichuan Basin (16,000 samples), Recursive Feature Elimination (RF-RFE) was used to identify nine key factors, including Stoneley wave slowness and caliper, from 30 feature parameters. Bayesian optimization was employed to fine-tune the hyperparameters of the XGBoost model globally. Results indicate that the XGBoost model performs optimally in predicting maximum horizontal principal stress (SHmax) and minimum horizontal principal stress (SHmin). It achieves R² values of 0.978 and 0.959, respectively, on the test set. The error metrics (MAE, MSE, RMSE) of the XGBoost model are significantly lower than those of SVM and Random Forest, demonstrating its precise capture of the nonlinear relationships between logging parameters and in situ stress. This framework enhances the model’s adaptability to complex geological conditions through multi-well data training and eliminating redundant features, providing a reliable tool for hydraulic fracturing design and wellbore stability assessment in shale gas development.

Characterization of shale pore architecture forms the scientific basis for effective shale gas exploitation. Deep LMX FM shale from the Luzhou area was analyzed using SANS, LTPA, XRD, and SEM. This study quantitatively characterized the pore structure, focusing on closed-pore development and connectivity, and explored lithological controls. Pore-size distribution shows that micropores and small mesopores dominate the pore volume, with an average median pore diameter of 5.17 nm. Closed pores are abundant, indicated by a high average closed-pore ratio of 28.98%, reflecting generally poor connectivity. Pores smaller than 5 nm contribute 88.12% of the total SSA. Both pore volume and SSA correlate positively with TOC. In organic-rich and moderately organic-rich siliceous shales, these parameters also correlate positively with quartz content. In contrast, for organic-rich mixed shales, they correlate positively with clay mineral content. Among the lithofacies, organic-rich siliceous shale possesses relatively larger pore volume and SSA, along with better pore connectivity, making it the most favorable reservoir facies. Based on pore-structure characteristics and the regional structural setting, we recommend adopting close-spacing hydraulic fracturing with reduced cluster spacing in structurally stable areas to enhance stimulation. In structurally complex areas, engineering designs should prioritize risk mitigation to ensure operational success.

Based on continuous production data from 112 wells in a deep shale gas block, comprising approximately 160,000 production days, this study proposes a hybrid deep learning framework integrating the Rabbit Optimization Algorithm (ROA), Transformer, and Mamba architectures for daily shale gas production forecasting. Average casing pressure, daily water production rate, and flowback ratio are employed as input features, while ROA is used to globally optimize model hyperparameters. Model performance and robustness are evaluated using five-fold cross-validation. compared with the standalone Mamba model, the proposed ROA-Transformer–Mamba framework reduces RMSE from 0.0418 to 0.0328 (approximately 21.5%) and MAE from 0.02438 to 0.0174 (approximately 28.6%), while increasing the coefficient of determination to 0.938., demonstrating superior prediction accuracy and generalization capability. To the best of the authors’ knowledge, this is the first study to integrate ROA, Transformer, and Mamba architectures for shale gas production forecasting, providing an effective data-driven solution for capturing complex production dynamics under multi-factor coupling conditions.