Abstract: [Purpose] The current industry evaluation standards for geothermal reservoirs are formulated based on hydrothermal with excellent storage capacity (medium-high porosity and medium-high permeability). According to these standards, most of the geothermal reservoirs in China's basins are classified as hydrothermal reservoirs with poor storage capacity (characterized by medium-low porosity and low permeability), thus making the existing standards inapplicable. In addition, during the pre-feasibility exploration stage of geothermal resources, the commonly used evaluation methods require a large number of parameters and data. However, the information available at this stage is often limited. Therefore, it is necessary to explore rapid evaluation methods with fewer parameters. The hydrothermal sandstone reservoirs in the key oil areas of the Songliao Basin have the characteristics of medium-thin layers, medium-low porosity, and low permeability. They belong to hydrothermal with excellent storage capacity. Taking these as an example, this paper will study the evaluation standards of hydrothermal with poor storage capacity reservoirs and explore rapid evaluation methods for reservoirs under the condition of fewer parameters. [Methods] Firstly, based on understanding the basic characteristics of the thickness, porosity, and permeability of the target layer reservoir, statistical analysis was conducted. The golden section method was applied to classify the levels and determine the evaluation standards for these parameters. Secondly, since the temperature differences of the geothermal reservoirs are not significant and they all belong to low-temperature reservoirs, the daily production of a single well is more important for the evaluation of the geothermal reservoir. The thickness of the sand bodies, porosity, permeability, and daily production of a single well in the development area are selected. A multivariate linear regression analysis is used to analyze these data, construct a geothermal reservoir evaluation formula, calculate the scores of each evaluation unit, and divide the grades according to the golden section method. [Results] The score ranking of this method was compared pairwise with the score rankings of two existing evaluation methods. It was found that incorporating the thickness of the sand bodies while discarding temperature is reasonable. Moreover, the standard deviation of the scores obtained by this method is relatively large, which can better reflect the differences among the evaluation units. Therefore, this method is more feasible, and the evaluation criteria formulated for hydrothermal with poor storage capacity are more reasonable. [Significance] The evaluation criteria for hydrothermal with poor storage capacity determined in this paper have certain reference significance for the evaluation of geothermal reservoirs in other basins. Considering the differences between basins, the few-parameter rapid evaluation method determined in this paper may not be applicable to other basins. However, the process of determining the evaluation formula is worth learning from.