Tar-rich coal integrates the properties of coal, oil and gas. Promoting its exploration and development has important strategic value for ensuring the supply of oil and gas resources in China, and realizing the clean and efficient utilization of coal. Therefore, based on the data of tar yield, industrial components, elemental analysis and coal petrography analysis of coal samples in Tiaohu sag of Santanghu Basin, combined with logging response, a logging prediction model of tar-rich coal tar yield is established, and the tar-rich coal resources of Badaowan formation in the study area are predicted. The results show that: the yield of coal tar is positively correlated with volatile yield, hydrogen content and vitrinite content, and negatively correlated with ash yield and inertinite content. There is a good negative correlation between volatile yield, hydrogen content, vitrinite content and acoustic time difference, compensated density logging values, and a poor correlation with natural gamma logging values. In addition, the prediction model of coal tar yield based on machine learning is established. The correlation coefficient between the predicted value and the actual value is 0.92. The relative error of tar yield prediction results of 90% coal samples is less than 20%, and the relative error of 75% coal samples is less than 15%.