Background Geothermal energy in porous sandstone reservoirs is recognized as stable, efficient clean energy. However, these reservoirs feature heterogeneity and exploration uncertainty, posing serious challenges to probabilistic resource assessment and the identification of suitability for resource development.
Methods This study investigated the geothermal resources in the Guantao Formation within the northwest Shandong plain. Using the volumetric method and Monte Carlo simulation, this study assessed the uncertainty of the resource potential. Then, considering the characteristics of geothermal resources, the attributes of geological structures, and the social economy, this study developed a multi-dimensional evaluation index system for the suitability of sandstone geothermal reservoirs. The weights of the evaluation indices were calculated using the game-based combination weighting method. Finally, this study conducted a quantitative suitability evaluation of the geothermal resources and verified the evaluation results based on the spatial distribution of geothermal wells.
Results and Conclusions The results indicate that geothermal reservoirs in the Guantao Formation within the northwest Shandong plain hold maximum probabilistic geothermal resources of 5.68×1017 kJ, corresponding to coal equivalent of 1.94×1010 t. These reservoirs exhibit a 90% probability of geothermal resources of (4.75‒6.32)×1017 kJ and an average geothermal resource abundance of 3.35×1013 kJ/km2. Within the study area, zones with relatively high and high suitability cover an area of 5 557 km2, representing 35.63% of the total area. In contrast, zones with low suitability manifest the lowest areal proportion (merely 14.32%). The comprehensive analysis of the resource potential and suitability evaluation results reveals that the Dezhou buried fault depression in the northwest Shandong plain serves as the preferred target area for geothermal exploration and exploitation. The results of this study will provide technical support for the exploration optimization and target area identification of geothermal resources in key areas.