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The mechanical conditions that allow seismogenic faults to be activated in distinctive positions and moments remain uncertain. An integrated approach is proposed to quantify the hydrological connection between stimulated wells and associated seismogenic faults during fracturing stimulation. A case study in Fox Creek, Alberta, was conducted to demonstrate the applicability of this integrated approach. Ant tracking was used to identify the subvertical basement-rooted faults from a three-dimensional seismic reflection survey. A coupled flow–geomechanics simulation was conducted to quantify the well–fault hydraulic connection in terms of pore pressure diffusion and poroelastic stress perturbation during fracturing stimulation. Three identified basement-rooted faults were corroborated by the focal mechanisms and spatial distribution of induced siemsicity events. The negative shear stress gradient indicates the downward shear growth during hydraulic connections. The induced earthquakes were triggered by fluid diffusion through hydraulic fractures along high-permeability fault damage zones downwards into the basement. This basal fault slip was attributed primarily to the elevated pore pressure along the fault plane in response to fracturing fluid injection. The moderate distance (879 m for NS-oriented wells and 749 m for NW-SE-oriented wells) between the future horizontal wellbores and critically-stressed faults could mitigate the effects of well-fault hydraulic connection and reduce the seismicity risks. • Ant-tracking interpretation provides geological evidence for spatial distribution of HF-induced seismicity. • Earthquakes were induced by fluid diffusion along high-permeability damage zones of basement-rooted faults. • The moderate distance between fracturing wellbore and critically-stressed fault could reduce seismicity risks. [ABSTRACT FROM AUTHOR]

Hundreds of horizontal wells have been performed fracturing operations to exploit the unconventional shale gas resources in the Duvernay Formation of Fox Creek, Alberta. Despite achieving the practical analysis of shale gas production via the data-mining approach, previous studies failed to incorporate comprehensive site-specific geological and operational factors. In this study, a comprehensive machine-learning approach is developed to forecast the shale gas production via the integration of geological and operational factors. Thirteen geological and operational parameters deriving from the well logging, core experiment and treatment data are included as the input variables, whereas the 12-month shale gas production is regarded as the target variable. Results show that factors that mostly contributed to the shale gas production are found to be total fluid injection, total proppant mass, well TVD, permeability, y coordinate, porosity, gas saturation, number of stages, x coordinate, formation pressure, horizontal length, distance to fault and Duvernay thickness. Four machine learning methods are evaluated, where the Extra Trees approach has led to the highest coefficient of determination R2 of 0.81. Case study for Well 2 have shown that the shale gas production can be doubled if increase the total pumped volume and proppant placed mass by approximately 73% and 38%. • A machine-learning approach is proposed to forecast Duvernay shale gas production. • Injection, proppant, TVD, permeability, coordinate, porosity, saturation is important. • Extra Tree method has led to the highest coefficient of determination R2 of 0.809 • Well 2 production can be doubled if increase injection and proppants by 73% and 40%. [ABSTRACT FROM AUTHOR]