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To deeply understand the effects of support properties on the performance of Mo-based slurry-phase hydrocracking catalysts, four Mo-based catalysts supported on amorphous silica alumina (ASA), γ-Al2O3, ultra-stable Y (USY) zeolite and SiO2 were prepared by the incipient wetness impregnation method, respectively, and their catalytic performances were compared in the vacuum residue (VR) hydrocracking process. It is found that the Mo/ASA catalyst exhibits the highest VR conversion among the different catalysts, indicating that both the appropriate amount of acid sites, especially B acid sites and larger mesoporous volume of ASA can enhance the VR hydrocracking into light distillates. Furthermore, Mo catalysts supported on the different supports show quite different product distributions in VR hydrocracking. The Mo/ASA catalyst provides higher yields of naphtha and middle distillates and lower yields of gas and coke compared with other catalysts, it is attributed to the highest MoS2 slab dispersion, the highest sulfuration degree of Mo species, and the most Mo atoms located at the edge sites for the Mo/ASA catalyst, as observed by HRTEM and XPS analyses. These features of Mo/ASA are beneficial for the hydrogenation of intermediate products and polycyclic aromatic hydrocarbons to restrict the gas and coke formation. [ABSTRACT FROM AUTHOR]

A comprehensive dataset from 594 fracturing wells throughout the Duvernay Formation near Fox Creek, Alberta, is collected to quantify the influences of geological, geomechanical, and operational features on the distribution and magnitude of hydraulic fracturing-induced seismicity. An integrated machine learning-based investigation is conducted to systematically evaluate multiple factors that contribute to induced seismicity. Feature importance indicates that a distance to fault, a distance to basement, minimum principal stress, cumulative fluid injection, initial formation pressure, and the number of fracturing stages are among significant model predictors. Our seismicity prediction map matches the observed spatial seismicity, and the prediction model successfully guides the fracturing job size of a new well to reduce seismicity risks. This study can apply to mitigating potential seismicity risks in other seismicityfrequent regions. [ABSTRACT FROM AUTHOR]