Abstract: To achieve efficient exploration and development of sandstone reservoirs in the eastern South China Sea, there is an urgent need to determine the formation micro-imaging (FMI) response characteristics of sandstones with different grain sizes. This effort will help overcome the limitations of conventional logging methods in distinguishing medium-grained sandstones, fine-grained sandstones, siltstones, and mudstones, thereby providing a basis for the quantitative evaluation of reservoir pore structures. This study investigated typical intervals of medium-grained sandstones, fine-grained sandstones, siltstones, and mudstones in well A in the Enping area. Specifically, a systematic statistical analysis was conducted for the FMI data of these rocks. By calculating the average resistivity, standard deviation, and coefficient of variation (C_V) and combining shale content and porosity parameters, this study conducted a correlation analysis of the FMI response characteristics. Accordingly, a chart board for lithology classification and a porosity prediction model were established. The results show that different rocks exhibited distinct FMI response characteristics. The average resistivity of various sandstones decreased in the order of medium-grained sandstones, fine-grained sandstones, siltstones, and mudstones, with image heterogeneity differing significantly. The siltstones and mudstones exhibited markedly higher C_V values than medium- and fine-grained sandstones, aligning with the mottled texture observed in FMI images. Based on these findings, a comprehensive chart board for lithology classification was innovatively developed. Furthermore, a multivariate statistical model relating porosity to average resistivity and C_V was created, achieving a goodness-of-fit of 0.837. Therefore, methods for lithology classification and semi-quantitative porosity prediction based on macroscopic FMI characteristics were achieved. Overall, this study clarifies the patterns of FMI responses of sandstones with different grain sizes, and the proposed methods for lithology classification and porosity prediction can effectively address the challenge of lithology differentiation using conventional logging. The results of this study provide a reliable technical reference for the evaluation of sandstone reservoirs in the eastern South China Sea.