The Research on Complex Lithology Identification Based on Well Logs: A Case Study of Lower 1st Member of the Shahejie Formation in Raoyang Sag

Zhaojing Song, Dianshi Xiao (), Yongbo Wei, Rixin Zhao, Xiaocheng Wang and Jiafan Tang
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Zhaojing Song: Shandong Provincial Key Laboratory of Deep Oil and Gas, Qingdao 266580, China
Dianshi Xiao: Shandong Provincial Key Laboratory of Deep Oil and Gas, Qingdao 266580, China
Yongbo Wei: Exploration and Development Research Institute of Daqing Oilfield Co., Ltd., Daqing 163712, China
Rixin Zhao: Shandong Provincial Key Laboratory of Deep Oil and Gas, Qingdao 266580, China
Xiaocheng Wang: Shandong Provincial Key Laboratory of Deep Oil and Gas, Qingdao 266580, China
Jiafan Tang: Shandong Provincial Key Laboratory of Deep Oil and Gas, Qingdao 266580, China

Energies, 2023, vol. 16, issue 4, 1-19

Abstract: Lithology identification is the basis for sweet spot evaluation, prediction, and precise exploratory deployment and has important guiding significance for areas with low exploration degrees. The lithology of the shale strata, which are composed of fine-grained sediments, is complex and varies regularly in the vertical direction. Identifying complex lithology is a typical nonlinear classification problem, and intelligent algorithms can effectively solve this problem, but different algorithms have advantages and disadvantages. Compared were the three typical algorithms of Fisher discriminant analysis, BP neural network, and classification and regression decision tree (C&RT) on the identification of seven lithologies of shale strata in the lower 1st member of the Shahejie Formation (Es 1 L ) of Raoyang sag. Fisher discriminant analysis method is linear discriminant, the recognition effect is poor, the accuracy is 52.4%; the accuracy of the BP neural network to identify lithology is 82.3%, but it belongs to the black box and can not be visualized; C&RT can accurately identify the complex lithology of Es 1 L , the accuracy of this method is 85.7%, and it can effectively identify the interlayer and thin interlayer in shale strata.

Keywords: lithology identification; intelligent algorithms; well logs; shale strata; Raoyang sag (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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