Author(s)

Tiantian Wu¹, Xin Bai^2,3, Fei Shang⁴, Haiyan Zhou⁴, Lan Wang⁴, Xuexian Zhou⁴, Zhi Zhong¹, Zhi Yang⁴, Jinyou Zhang⁵, Xinyang Cheng³, Peiyu Zhang⁶, Ruiqian Chen^2,3

¹Laboratory Information and Internet, China University of Geosciences, Wuhan, China.
²China University of Petroleum (Beijing), Beijing, China.
³State Key Laboratory of Oil and Gas Resources and Exploration, Beijing, China.
⁴Research Institute of Petroleum Exploration & Development, Beijing, China.
⁵Daqing Oilfield Company, Daqing, China.
⁶Qingdao Liquefied Natural Gas Co., Ltd., Qingdao, China.

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 lot of calcareous and siliceous thin interlayers in Qingshankou Formation, and TOC content is generally between 2% and 3%. Ro is the highest in Gulong sag, followed by Sanzhao sag. The lithofacies mainly consists of felsic shale and mixed shale, mainly in the first member of Qingshankou Formation. Comprehensive analysis shows that shale oil development potential is enormous in the eastern part of Sanzhao Sag and the northern part of Gulong Sag.

Songliao Basin, Qingshankou Formation, Shale Oil, Sweet Spot, Artificial Neural Network

Wu, T. , Bai, X. , Shang, F. , Zhou, H. , Wang, L. , Zhou, X. , Zhong, Z. , Yang, Z. , Zhang, J. , Cheng, X. , Zhang, P. and Chen, R. (2023) Quantitative and Comprehensive Prediction of Shale Oil Sweet Spots in Qingshankou Formation, Songliao Basin. Journal of Geoscience and Environment Protection, 11, 290-315. doi: 10.4236/gep.2023.115018.