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Abstract: The Bozhong Sag is the largest petroliferous sag in the Bohai Bay Basin, and the source rocks of Paleogene Dongying and Shahejie Formations were buried deeply. Most of the drillings were located at the structural high, and there were few wells that met good quality source rocks, so it is difficult to evaluate the source rocks in the study area precisely by geochemical analysis only. Based on the Rock-Eval pyrolysis, total organic carbon (TOC) testing, the organic matter (OM) abundance of Paleogene source rocks in the southwestern Bozhong Sag were evaluated, including the lower of second member of Dongying Formation (E3d2L), the third member of Dongying Formation (E3d3), the first and second members of Shahejie Formation (E2s1+2), the third member of Shahejie Formation (E2s3). The results indicate that the E2s1+2 and E2s3 have better hydrocarbon generative potentials with the highest OM abundance, the E3d3 are of the second good quality, and the E3d2L have poor to fair hydrocarbon generative potential. Furthermore, the well logs were applied to predict TOC and residual hydrocarbon generation potential (S2) based on the sedimentary facies classification, using ΔlogR, generalized ΔlogR, logging multiple linear regression and BP neural network methods. The various methods were compared, and the BP neural network method have relatively better prediction accuracy. Based on the pre-stack simultaneous inversion (P-wave impedance, P-wave velocity and density inversion results) and the post-stack seismic attributes, the three-dimensional (3D) seismic prediction of TOC and S2 was carried out. The results show that the seismic near well prediction results of TOC and S2 based on seismic multi-attributes analysis correspond well with the results of well logging methods, and the plane prediction results are identical with the sedimentary facies map in the study area. The TOC and S2 values of E2s1+2 and E2s3 are higher than those in E3d3 and E3d2L, basically consistent with the geochemical analysis results. This method makes up the deficiency of geochemical methods, establishing the connection between geophysical information and geochemical data, and it is helpful to the 3D quantitative prediction and the evaluation of high-quality source rocks in the areas where the drillings are limited.