Chao Zhang

Terrestrial heat flow plays a vital role in determining the present thermal regimes of sedimentary basins, offering a robust foundation for understanding hydrocarbon maturation processes and the geothermal resource potential. The Junggar basin is one of the largest and most petroliferous superimposed petroleum basins in China. However, research on heat flow is scarce. In this study, 94 new high-quality heat flow values are derived from through borehole temperature analysis and thermal conductivity measurements of rocks. The results indicate that (1) the geothermal gradient in the basin varies from 11.4 to 28.3 °C km⁻¹, with a mean value of 20.9 ± 3.4 °C km⁻¹, and the heat flow varies from 23.4 to 64.5 mW m⁻², with a mean value of 45.1 ± 8.4 mW m⁻². The overall low geothermal gradient and heat flow are attributed to the continuous cooling during the Meso-Cenozoic. (2) At basin scale, the high heat flow values are primarily concentrated in areas characterized by basement uplift, whereas the low heat flow values are mainly located in the depressions. This suggests that thermal refraction is the primary factor influencing the heat flow variations. (3) Although large-scale development and utilization of geothermal resources face challenges, certain local areas in the basin show promise for geothermal resource utilization.

Efficient exploration of geothermal resources is the basis of exploitation and utilization of geothermal resources. In recent years, Geographic Information System (GIS) has been increasingly used for the exploration owing to its power ability to integrate and analyze multiple sources of data related to the formation of geothermal resources, such as geology, geophysics, and geochemistry. Correctly understanding the control effect of evidence factors on geothermal resources is the premise and basis of whether the prediction results of evidence weight model are accurate. Traditionally, the conventional weight of evidence model assume that each evidence factor exerts a uniform controlling effect on the formation and distribution of geothermal resources. However, recent research indicates significant variations in the controlling ability of factors such as faults and granites, influenced by factors like activity levels and crystalline ages. Yet, studies addressing this differential control are lacking. To address this gap, we propose a series of weight of evidence models using abundant geological, geophysical, and geothermal data from the western Sichuan plateau, a high-temperature geothermal hotspot in China. This study aims to investigate the impact of varying controlling abilities of evidence factors on the evaluation model, with faults and granites as a case. Performance metrics include prediction rate, success rate index, receiver operating characteristic curve (ROC) and prediction rate of geothermal well. The findings of this research reveal that the weight of evidence model developed through the methodology outlined in this study exhibits superior performance compared to the conventional weight of evidence model. This superiority is evidenced by higher prediction rates, success indices, prediction rate of geothermal wells, and larger AUC values of ROC. Among these models, the weight of evidence model considering both fault and granite classification have the best performance in model evaluation indicators, with a prediction rate of 22.528 and a success index of 0.015408 in the very high potential area. The prediction rate and success index of the high potential area are 3.656 and 0.0025, respectively, and the prediction rate and success index of the middle potential area are 1.649 and 0.001128, respectively, and the AUC value is 0.808, indicating that the model has good accuracy. In terms of geothermal well prediction, the total prediction rate of geothermal favorable areas based on fault and granite classification evidence weight model is as high as 47.0526. Therefore, when constructing the weight of evidence model, the influence of the difference control of evidence factors on the formation of geothermal resources should be fully considered. These results underscore the effectiveness of the proposed methodology in enhancing the predictive accuracy and reliability of geothermal resource assessment in this study. Based on the prediction results of the weight of evidence model considering both fault and granite classification, four favorable geothermal areas with abundant surface heat display are identified in this paper, namely Kangding, Litang, Batang and Ganzi-Dege. In addition, the relatively weak surface heat display areas such as Jiulong, Daofu, Luhuo and Derong also show high geothermal potential. Some attention should be paid to geothermal exploration in the future.