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This study employs the Muller geological accumulation index method and the Hakanson risk assessment method in combination with ArcGIS technology to assess the potential heavy metal pollution in soil. The study aims to investigate the geochemical characteristics, pollution degree, and potential ecological risks associated with soil heavy metal element content and distribution across various geological formations. The concentrations of eight heavy metals, namely Cu, Cd, Ni, Pb, Zn, Hg, As, and Cr, were analysed by collecting samples of the soil layer, parent material layer, and parent rock layer. The proportions of samples with different risk levels are accurately calculated to locate areas with high pollution risks. The findings are as follows: Firstly, the soil pH value in the study area is low, and the Cd and Cr concentrations are 1.38 and 1.20 times the background concentrations in soil in Sichuan Province, respectively. Moreover, Cu, Cd, Ni, Zn, and Cr concentrations are 1.23, 1.10, 1.20, 1.10, and 1.55 times the background concentrations in soil in China. Secondly, the distribution of heavy metal elements varies across different geological formation units, and the heavy metal contents of different samples from the same formation also varies significantly. The concentrations of heavy metal elements are influenced by different geological formations, with the pedogenic parent rock in the soil determining the initial concentrations of heavy metal elements. Thirdly, the spatial distribution of heavy metal elements indicates a good spatial distribution coupling relationship among the soil layer, parent material layer, and rock layer. The study suggests that the contents of the eight heavy metal elements in the soil inherited the characteristics of the parent material to a large extent and are influenced and controlled by the pedogenic parent rock (geological formation). Finally, the evaluation results reveal that all eight heavy metal elements exhibit moderate pollution, with a relatively large distribution area exhibiting moderate Cd, Hg, and As pollution and above. The overall study area exhibits a mild potential ecological risk level, with some areas at a moderate potential ecological risk level, distributed in patches across the study area. Cd and Hg exhibit relatively high potential ecological risks. The potential ecological risks of heavy metals are relatively high in the JKms and Tss units. While the overall quality of the soil in the study area is good, heavy metal pollution and potential ecological risks of some elements are still above ideal levels. [ABSTRACT FROM AUTHOR]

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]