The diagnosis of overflow risk has always been an important area of research in drilling operations in the field of oil and gas engineering. In the face of the limitations and lag of traditional overflow diagnosis methods, the practical application effect of existing models and methods is not obvious, and there is no integration of the physical model and the intelligent algorithm model for overflow diagnosis, this paper proposes a method of adaptive weight fusion of physical model and intelligent algorithm model diagnosis results. Based on the fusion of the physical model and the intelligent algorithm model, an overflow diagnosis model of managed pressure drilling is established. The research results show that the fusion model in this paper can combine the accuracy weight of the physical model and the intelligent algorithm model for the intelligent diagnosis of overflow risk, which improves the mechanization and interpretability of the model and diagnosis results while ensuring accuracy and efficiency. And the intelligent algorithm model used in the fusion model is superior to other algorithm models. The overflow diagnosis accuracy of the fusion model on the test set samples reaches more than 98%, and the accuracy of the validation set is 94.25%. The content of this study provides guidance for drilling overflow diagnosis and model fusion methods.

In cementing operation, the “cement-–rock” interface bonding ability is an important factor affecting the long-term sealing ability of cement sheath, and the evolution mechanism of the interface sealing ability needs to be further studied. However, due to the particularity of the interface transition area, it is difficult to comprehensively analyze the macroscopic acoustic properties, mechanical properties and sealing evolution ability of the interface due to the lack of precise quantitative testing equipment. To solve the above problems, a cement-sheath cementing-interface sealing capability evolution evaluation device was developed, and a laboratory acoustic response accuracy experiment was carried out to form a quantitative evaluation method of cement–formation interface capability. The developed apparatus withstood pressures up to 30 MPa and temperatures up to 150 °C during basic performance testing. The ultrasonic probes were calibrated using standard cylindrical steel specimens, with P- and S-wave measurement accuracies exceeding 95%. Preliminary cement stone–sandstone acoustic calibration was subsequently performed at 17 °C and 9.8 MPa for 9 days, corresponding to the conditions of the selected field interval. Comparison of the normalized laboratory acoustic response with representative field VDL data showed an agreement of approximately 86.8%. These results provide preliminary validation of the basic operational and acoustic measurement capabilities of the developed platform; however, cement stone–rock interfacial behavior under a broader range of temperature and pressure conditions remains to be systematically validated.

Coal slime, a byproduct of coal processing with high ash content, poses significant challenges in terms of its efficient separation and resource utilization due to its fine particle size and complex composition. This study aims to optimize the oil agglomeration process for coal slime separation through systematic parameter investigation and predictive modeling. Response surface methodology (RSM) was employed to analyze the individual and interactive effects of pulp density, oil dosage, and agitation rate on three key performance indicators: combustible recovery, efficiency index, and ash rejection. Meanwhile, an artificial neural network (ANN) was developed to establish a robust prediction model for the efficiency index. The novelty of this work lies in the integration of thermodynamic analysis, multi-objective optimization, and machine learning approaches. The key findings include the identification of dodecane as the optimal bridging liquid due to its intermediate carbon chain length that balances interfacial tension and wettability. Under optimized conditions (14% pulp density, 22% oil dosage, and 1600 r/min), the process achieved a combustible recovery of 91.49%, ash rejection of 61.58%, and efficiency index of 53.07%. The ANN model demonstrated superior predictive capability with an overall R² of 0.9659 and RMSE of 1.12. This work provides comprehensive guidelines for the design, optimization, and scale-up of coal slime oil agglomeration processes in industrial applications.

Oil agglomeration, as an efficient technique for coal slime cleaning and upgrading, was employed to separate coal slime with an ash content of 19.08% in this work. The optimum oil type, the pulp density, the oil dosage, and the agitation rate were determined at the dodecane, 12%, 24%, and 1600 r/min, respectively. The response surface methodology (RSM) was adopted to investigate the interactions between various operational factors on the response of combustible material recovery, efficiency index, and ash rejection. By considering the interactions among operational factors, the agglomeration achieved improvement, given small oil consumption, medium agitation rate, and high processing capacity, through optimized operational conditions. Moreover, a prediction model with a higher prediction accuracy for the efficiency index of coal slime oil agglomeration was established based on the artificial neural network (ANN). This work provides an experimental foundation for the operation design and process optimization in the oil agglomeration of coal slimes.

Heavy oil reserves constitute over 70% of global crude oil resources, yet their extraction remains challenging due to high viscosity and poor flowability. Multicomponent thermal fluid (MTF) technology, integrating CO₂, N₂, and steam, offers a promising solution by enhancing emulsification and mobility. This study employs molecular dynamics (MD) simulations to unravel the microscale emulsification mechanisms of heavy oil under MTF. By systematically varying gas-to-oil ratios (R), we identify favorable ratios of R = 1.3 for CO₂ and R = 0.85 for N₂, validated through analyses of diffusion coefficients and radial distribution function (RDF). The addition of MTF increased the diffusion coefficient of the oil droplets to 0.9565 × 10^–8 m²/s, which is 56.4% higher than the effect of the CO₂ system and 85.6% higher than the effect of the N₂ system. This improved transport behavior correlates with emulsion stabilization mechanisms evidenced by distinct RDF peaks at interfacial separation distances of 0.40 nm (Oil-CO₂) and 0.51 nm (Oil-N₂). The results demonstrated that the combination of CO₂, N₂, and H₂O significantly facilitated the dispersion of oil droplets and enhanced emulsification. The findings will offer microlevel guidance and theoretical support for efficient heavy oil extraction.