松辽盆地青山口组页岩油甜点定量综合预测

F. Shang, Xin Bai, Haiyan Zhou, Lan Wang, Xuexian Zhou, Tiantian Wu, Zhi Zhong, Zhi-xia Yang, Jinyou Zhang, Xinyang Cheng, Peiyu Zhang, Ruiqian Chen
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引用次数: 0

摘要

松辽盆地青山口组泥页岩是主要的岩源,蕴藏着丰富的页岩油资源。页岩油的成功开发取决于“甜点”的评价和优化。为准确识别和优选松辽盆地青山口组页岩油有利甜点,对原始测井资料进行了预处理。利用处理后的测井资料,对青山口组薄泥页岩夹层进行了有效识别。基于人工神经网络方法,对青山口组泥页岩矿物含量进行了预测。根据矿物和TOC含量确定了岩相。最后,建立了松辽盆地青山口组总有机碳(TOC)、镜质体反射率(Ro)、矿物含量和岩石的三维模型,对研究区页岩油有利甜点区进行了评价和预测。结果表明,存在一种
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Quantitative and Comprehensive Prediction of Shale Oil Sweet Spots in Qingshankou Formation, Songliao Basin
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
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