An intelligent lithology identification method for sandstone and mudstone strata and its applications: A case study of the Jurassic strata in the Lunnan area, Xinjiang, China

Authors

CAI Ming, Key Laboratory of Exploration Technologies for Oil and Gas Resources, Ministry of Education, Yangtze University, Wuhan 430100, China; College of Geophysics and Petroleum Resources, Yangtze University, Wuhan 430100, ChinaFollow
ZHOU Qingwen, Key Laboratory of Exploration Technologies for Oil and Gas Resources, Ministry of Education, Yangtze University, Wuhan 430100, China; College of Geophysics and Petroleum Resources, Yangtze University, Wuhan 430100, China
YANG Cong, Wuhan Safety & Environmental Protection Research Institute Co., Ltd., Sinosteel, Wuhan 430081, China
CHEN Feng, College of Geophysics and Petroleum Resources, Yangtze University, Wuhan 430100, China; Anhui Provincial Coal Geological Exploration Bureau Third Team, Suzhou 234000, China
WU Dong, Information Center, CNPC Engineering Technology R&D Company Limited, Beijing 102206, China
LIN Wang, Information Center, CNPC Engineering Technology R&D Company Limited, Beijing 102206, China
ZHANG Chengguang, Key Laboratory of Exploration Technologies for Oil and Gas Resources, Ministry of Education, Yangtze University, Wuhan 430100, China; College of Geophysics and Petroleum Resources, Yangtze University, Wuhan 430100, ChinaFollow
ZHANG Yuanjun, Key Laboratory of Exploration Technologies for Oil and Gas Resources, Ministry of Education, Yangtze University, Wuhan 430100, China; College of Geophysics and Petroleum Resources, Yangtze University, Wuhan 430100, China
MIAO Yuxin, Key Laboratory of Exploration Technologies for Oil and Gas Resources, Ministry of Education, Yangtze University, Wuhan 430100, China; College of Geophysics and Petroleum Resources, Yangtze University, Wuhan 430100, China

Abstract

Objective Lithology identification lays the foundation for fine-scale reservoir evaluation. However, traditional identification methods generally utilize the interactive relationships between only 2‒3 logging parameters, suffering from low utilization rates of logging information and low identification accuracy for strata with small differences in logging responses. This seriously restricts the effects of old well reexamination. The efficient, intelligent CatBoost classification algorithm can fully mine the correlations between multi-source logging information and lithology. Methods This study investigated the Jurassic sandstone and mudstone reservoirs in the Lunnan area, Xinjiang, China. Using five logging parameters determined through sensitivity analysis, i.e., natural gamma-ray value, spontaneous potential, deep and shallow resistivity ratio, sonic interval transit time, and density, this study developed an intelligent lithology identification model based on the CatBoost algorithm. The optimized model was employed to deal with actual logging data for lithology identification, and its performance was evaluated using accuracy, precision, and recall and was then compared with the lithology identification results of the random forest (RF) and k-nearest neighbors (KNN) algorithms. Results and Conclusions The results indicate that the large-scale lithologies of the Jurassic strata in the Lunnan area include mudstones, sandstones, and conglomerates, with complex fine-scale lithologies. In the identification of the fine-scale lithologies of the target reservoir, the intelligent lithology identification model, established using the CatBoost algorithm and lithology-sensitive logging parameters, yielded an accuracy of 92.64%, significantly higher than that of the random forest model (82.95%) and the KNN model (70.16%). This result demonstrates that the CatBoost model can effectively address of the challenges of lithology identification in the study area. The results of this study will provide a scientific basis for the review and further exploration and development of old wells in the Lunnan area. Besides, these results can serve as a valuable reference for research on methods for fine-scale identification of complex lithologies.

Keywords

logging, lithology identification, artificial intelligence (AI), CatBoost, gradient boosting algorithm

DOI

10.12363/issn.1001-1986.24.07.0503

Recommended Citation

CAI Ming, ZHOU Qingwen, YANG Cong, et al. (2025) "An intelligent lithology identification method for sandstone and mudstone strata and its applications: A case study of the Jurassic strata in the Lunnan area, Xinjiang, China," Coal Geology & Exploration: Vol. 53: Iss. 1, Article 20.
DOI: 10.12363/issn.1001-1986.24.07.0503
Available at: https://cge.researchcommons.org/journal/vol53/iss1/20

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