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Assessment of production potential and prediction of sweet spots in unexploited shale gas wells are crucial technologies for achieving a high success rate in drilling. Most existing methods are only applicable for yield forecasting of shale gas wells with known geological and drilling/completion factors after production. In addition, the shale gas reservoir in the Longmaxi Formation in the southern Sichuan Basin of China is characterized by low porosity, low permeability, and diverse gas reservoir states. Thus, forecasting production from unexploited wells and predicting the exact location of the sweet spot under complex geological conditions have become challenging topic for both academia and industry. According to the first law of geography, shale gas wells that are closer in space have more similar geologic features. Based on this foundation, a machine-learning prediction model based on spatial correlation (SC-MLPM) is proposed for evaluating potential production of shale gas wells in the Changning play located in Longmaxi Formation in the southern Sichuan basin, China. Specifically, an improved K -nearest neighbor algorithm (SC-KNN) based on the spatial correlation characteristics of the exploited wells is designed to estimate the relevant geological factors of the unexploited wells in the same area. In addition, an extreme gradient lifting model based on Huber loss function (Hu-XGB) is developed to predict the potential production of unexploited shale gas wells, and then the shale gas sweet spot area in the research area is predicted quickly and accurately. The experimental results on 88 shale gas wells from Changning play show that the mean relative error of the prediction results of the proposed method is 9.8%. Furthermore, the results also determine that the sweet spots of shale gas wells in the research area are mainly developed in the northwest and northeast directions, and gradually become worse in the southeast direction. Eventually, experimental results for the Fox Creek play in Canada show that the proposed model obtains better predictive performance as the number of gas wells increases, and also verifies the generalizability. This work provides a basis for the efficient development of the research area. • A machine-learning prediction model based on spatial correlation is proposed. • An improved KNN algorithm is designed on the first law of geography. • An extreme gradient lifting model is developed to predict potential production. [ABSTRACT FROM AUTHOR]

To evaluate the risk of induced seismicity due to hydraulic fracturing, a detailed stress analysis in a seismically active region, i.e., the Changning shale-gas field in the southern Sichuan Basin, was conducted utilizing sonic wave velocity logs and high-resolution electrical resistivity image logs of shale gas wells. Wells with relatively higher magnitudes of horizontal stresses are spatially located in the vicinity of thrust faults, in agreement with the stress concentration at the upper tip of the basement-involved thrust fault. The prevailing orientation of maximum horizontal principal stress (S Hmax) is N110°–120°E on the southwestern Changning anticline, which aligns with the motion of the Sichuan Basin, as driven by the continuous eastward extrusion of the Tibet plateau, which initiated at approximately 50 Ma. However, the S Hmax orientation is N30°–70°E along the northern Changning anticline, implying an inheritance of the N–S geodynamic compression during the Jurassic to Late Cretaceous. Drilling-induced tensile fractures primarily appear in compliant layers that are rich in clay or organic matter in wells located adjacent to thrust faults, suggesting that stiff layers can efficiently inhibit the hydrofracture growth height owing to more intense tectonic stress near thrust faults. A natural fault with a strike of approximately 30° to the S Hmax orientation can be more easily activated by extreme pumping pressure in wells that are weakly affected by the stress concentration along the thrust fault. Nevertheless, a cyclic injection of fracturing fluid with a gradually increasing target pressure may effectively reduce the magnitude of induced seismicity. • Detailed stress analysis of Changning shale-gas field using geophysical data. • Wells with higher horizontal stresses spatially located near thrust faults. • Drilling-induced tensile fractures appear in layers rich in clay or organic matter. • Stiff layers inhibit hydrofracture growth due to more intense tectonic stress. • Cyclic fluid injection with increasing target pressure may reduce seismicity. [ABSTRACT FROM AUTHOR]