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 - Ming CAI; Qingwen ZHOU; Cong YANG; Feng CHEN; Dong WU; Wang LIN; Chengguang ZHANG; Yuanjun ZHANG; Yuxin MIAO (Meitian dizhi yu kantan)
ObjectiveLithology 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. MethodsThis 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.