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.

Shale gas is a critical energy resource, and estimating its ultimate recoverable reserves (EUR) is a key indicator for evaluating the development potential and effectiveness of gas wells. To address the challenges in accurately predicting shale gas EUR, this study analyzed production data from 200 wells in the CN block. Sixteen potential factors influencing EUR were considered, and key geological, engineering, and production factors were identified using Spearman correlation analysis and mutual information methods to exclude highly linearly correlated variables. An attention mechanism was introduced to weight input features prior to model training, enhancing the interpretability of feature contributions. The hyperparameters of the model were optimized using the Rabbit Optimization Algorithm (ROA), and 10-fold cross-validation was employed to improve the stability and reliability of model evaluation, mitigating overfitting and bias. The performance of four machine learning models was compared, and the optimal model was selected. The results indicated that the ROA-CatBoost-AM model exhibited superior performance in both fitting accuracy and prediction effectiveness. This model was subsequently applied for EUR prediction and for identifying the primary factors controlling productivity, providing effective guidance for development practices. The dominant factors and production forecasts determined by the model offer valuable references for optimizing block development strategies.