In the organic-rich shale deposited in the Chang 7₃ sub-member of the Triassic Yanchang Formation in the Ordos Basin, the frequent laminae lead to strong heterogeneity of shale series, which in turn affects the reservoir characteristics and oil-bearing property of the shale. Based on typical laminated shale samples, this paper quantitatively measured the laminae in the core with vernier calipers, and studied the development characteristics, laminae types and vertical distribution of laminae in the Chang 73 sub-member shale by combining microscopic thin section identification, total organic carbon determination, X-ray diffraction mineral analysis and rock pyrolysis experiments. The results show that the thickness of single layers of the laminae developed on the macro core is generally between 0.4 and 5.0 mm, and the ratio of the laminae to the core (the total thickness of the laminae/the core length of the statistical section) is between 5.7% and 16.8%, and the development frequency of the laminae is highly heterogeneous. Microscopically, it is divided into six types according to the morphology of laminae sedimentary structures, which are three kinds of straight laminae, corrugated laminae, granular laminae with continuous clay morphological sedimentary characteristics and three kinds of lenticular laminae, mottled laminae and weak laminae with discontinuous morphological sedimentary characteristics. The mineral composition and organic matter abundance of shale with different morphologies are obviously different. According to the microscopic material composition inside the laminae, they are mainly divided into organic laminae, felsic laminae and tuffaceous laminae, and according to the vertical superposition of different laminae, the laminated combinations are divided into homogeneous mudstone, felsic laminated shale and tuffaceous laminated shale. In a single well, the distribution of laminae is heterogeneous, and the development frequency of different laminae changes regularly with the changes of clay mineral content, total organic carbon(TOC) and oil saturation index(OSI).

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 (E₃d₂^L), 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 E₃d₂^L 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 E₃d₂^L, 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.