Quantitative and Comprehensive Prediction of Shale Oil Sweet Spots in Qingshankou Formation, Songliao Basin

Abstract

The mud shale of Qingshankou Formation in Songliao Basin is the main rock source and contains rich shale oil resources. The successful development of shale oil depends on evaluating and optimizing the “sweet spots”. To accurately identify and optimize the favorable sweet spots of shale oil in Qingshankou Formation, Songliao Basin, the original logging data were preprocessed in this paper. Then the thin mud shale interlayer of Qingshankou Formation was identified effectively by using the processed logging data. Based on the artificial neural network method, the mineral content of mud shale in Qingshankou Formation was predicted. The lithofacies were identified according to the mineral and TOC content. Finally, a three-dimensional (3-D) model of total organic carbon (TOC), vitrinite reflectance (Ro), mineral content, and rock of Qingshankou Formation in Songliao Basin was established to evaluate and predict the favorable sweet spots of shale oil in the study area. The results show that there are a