Evaluation of geothermal resource potential based on spatial data integration models: case study of Xiong’an New Area, North China

Abstract

As one of the most realistic and competitive renewable energy, geothermal resources are increasingly valued by governments and industries. Recognizing the favorable geothermal areas is conducive to reduce the economic risk of geothermal exploitation projects. This work is based on a case study from Xiong’an New Area, North China, which is rich in geothermal fluids in carbonate reservoirs. Three spatial data integration models are established to identify the geothermal favorable areas. The geothermal favorability map is developed based on five criteria layers generated from five evidence factors (bouguer gravity anomaly, magnetic anomaly, buried depth of carbonate reservoir, geothermal gradient and terrestrial heat flow). The performance of the model is evaluated by kappa coefficient analysis, success index analysis and receiver operating characteristic curve analysis. The results reveal that the Weighted Information Content Model is more effective and accurate. The extremely high favorable area and the high favorable area in Xiong’an New Area account for 6% and 8% of the total area, respectively. Moreover, the amount of thermal energy in geothermal favorable areas is determined by the unit volumetric method. The insights from this study provide a low-cost and efficient approach for evaluating the geothermal potential of regions lacking sufficient exploration data.