An intelligent safety risk assessment model is proposed by integrating the λ-fuzzy measure, Choquet integral, and triangular fuzzy numbers. It addresses the limitations of conventional methods like AHP that neglect nonlinear interactions among risk factors. The framework quantifies expert linguistic judgments to capture synergistic and substitutive relationships. Validation using two Sinopec seismic projects shows a 23.3% reduction in assessment time and an 18.1% accuracy improvement. Computed λ values (HB: −0.999997; SC: −0.999821) confirm strong substitutive interactions. Sensitivity analysis demonstrates robustness, with ±10% fuzzy measure variation causing <±3% output change. The model provides a computationally efficient, reliable tool for seismic acquisition and other complex industrial systems.

Geological storage of carbon dioxide in faulted sedimentary basins requires suitability evaluation methods that can address uncertainty, indicator interaction, and limited data availability. This study develops an integrated evaluation framework that combines the Analytic Hierarchy Process, triangular fuzzy numbers, and the Choquet fuzzy integral to assess basin-scale geological carbon dioxide storage suitability. The framework enables structured weight determination, explicit representation of expert uncertainty, and non-additive aggregation of interacting indicators. The evaluation focuses on deep saline aquifers in the Jianghan Basin and is based on seventeen indicators covering geological, structural, hydrogeological, and socio-economic conditions. The assessment integrates seismic interpretation, geological mapping, logging data, and published datasets, and is conducted at the level of tectonic units to support basin-scale screening. The method is applied to the Jianghan Basin using seventeen geological, structural, hydrogeological, and socio-economic indicators. The results indicate that burial depth primarily acts as a threshold condition, whereas caprock sealing capacity, fault system development, and hydrogeological stability dominate suitability differentiation. Interaction analysis reveals pronounced substitution effects among geological indicators, indicating that strong performance in key safety-related factors can compensate for less favorable secondary constraints during early-stage screening. The Qianjiang Sag and Jiangling Sag are identified as the most suitable storage units. The proposed framework provides a transparent and robust tool for basin-scale screening in structurally complex, data-limited sedimentary basins.

Formation pore pressure is a critical parameter controlling drilling safety and wellbore stability, and its prediction in ultra-deep carbonate formations is challenging due to extreme temperature–pressure conditions, complex geological settings, and strong lithological heterogeneity. This study develops a multi-parameter while-drilling process that integrates drilling engineering parameters, mud logging gas measurements, cuttings-based elemental logging data, and drilling fluid performance indicators to investigate the processes governing abnormal overpressure and its real-time responses. By combining elemental logging parameters, rock drillability indices, and gas logging responses, a predictive framework for detecting abnormal pore pressure is established. The results show that structural position strongly controls overpressure distribution; secondary fault zones preferentially host abnormal overpressure; synchronous enrichment of S and Sr in cuttings-derived elemental data provides precursor signals; and gas logging indicators, including total hydrocarbon peaks and hydrocarbon migration velocity, are highly sensitive to overpressure. Application of the proposed while-drilling process to three ultra-deep wells (Fudong-101, Hade-18, and TKe-1) generated nine real-time pressure warnings, eight of which were confirmed, yielding a prediction accuracy of 88.89%. These results demonstrate that the proposed process effectively improves real-time identification of abnormal overpressure in ultra-deep carbonate formations, enhancing drilling safety and operational efficiency.

Carbonate mound–shoal complexes, despite their complex pore structures and pronounced heterogeneity, represent one of the most productive reservoir units within carbonate formations. Accurately predicting key physical properties—such as porosity, permeability, and flow zone index—from well log data remains a significant challenge for conventional empirical methods. This study investigates the application of machine learning algorithms for optimizing the prediction of reservoir properties in hill-and-plain carbonate bodies. Six machine learning approaches—Support Vector Machines (SVM), Backpropagation Neural Networks (BPNN), Long Short-Term Memory Networks (LSTM), K-Nearest Neighbors (KNN), Random Forests (RF), and Gaussian Process Regression (GPR)—are systematically evaluated and compared. The analysis employed flow zone indices, geological data, and well log curves to classify porosity–permeability types. Seven logging parameters were used as input features: spectral gamma ray (SGR), uranium-free gamma ray (CGR), photoelectric absorption cross-section index (PE), bulk density (RHOB), acoustic travel time (DT), neutron porosity (NPHI), and true resistivity (RT). These features were paired with measured physical property values to train and validate the predictive models. Results demonstrate distinct algorithmic advantages for specific properties. The RF model achieved superior performance in permeability prediction, yielding an R² of 0.6824, whereas the GPR model provided the highest accuracy for porosity estimation, with an R² of 0.7342 and an Accuracy Index (ACI) of 0.9699. Despite these improvements, machine learning models still face limitations in accurately characterizing low-permeability zones within highly heterogeneous hill–terrace reservoirs. To address this challenge, the study integrates geological prior knowledge into the machine learning framework and applies cross-validation techniques to optimize model parameters, thereby providing a practical and robust approach for detailed assessment of mound–hoal carbonate reservoirs.