# Identification of Complicated Lithology with Machine Learning
###### Abstract  
Lithology identification is one of the most important research areas in petroleum engineering, including reservoir characterization, formation evaluation, and reservoir modeling. Due to the complex structural environment, diverse lithofacies types, and differences in logging data and core data recording standards, there is significant overlap in the logging responses between different lithologies in the second member of the Lucaogou Formation in the Santanghu Basin. Machine learning methods have demonstrated powerful nonlinear capabilities that have a strong advantage in addressing complex nonlinear relationships between data. In this paper, based on felsic content, the lithologies in the study area are classified into four categories from high to low: tuff, dolomitic tuff, tuffaceous dolomite, and dolomite. We also study select logging attributes that are sensitive to lithology, such as natural gamma, acoustic travel time, neutron, and compensated density. Using machine learning methods, XGBoost, random forest, and support vector regression were selected to conduct lithology identification and favorable reservoir prediction in the study. The prediction results show that when trained with 80% of the predictors, the prediction performance of all three models has improved to varying degrees. Among them, Random Forest performed best in predicting felsic content, with an MAE of 0.11, an MSE of 0.020, an RMSE of 0.14, and a R\({}^{2}\) of 0.43. XGBoost ranked second, with an MAE of 0.12, an MSE of 0.022, an RMSE of 0.15, and an R\({}^{2}\) of 0.42. SVR performed the poorest. By comparing the actual core data with the predicted data, it was found that the results are relatively close to the XRD results, indicating that the prediction accuracy is high. l 2025 202  
For instance, lithology identification models based on probabilistic and statistical analysis often depend heavily on prior information and are highly sensitive to assumptions about data distribution. Similarly, the crossplot method analyzes two-dimensional or three-dimensional intersections of logging parameters, but it relies strongly on the empirical judgment of geological experts and performs poorly in complex lithological transition zones. To sum up, these methods have significant limitations when identifying complex carbonate lithologies, and most can only distinguish a limited number of lithologies, including limestone and dolomite [2; 3; 4]. Due to the rapid development of machine learning technology, intelligent machine learning methods have been widely applied in lithology identification. Many scholars have used support vector machines [5; 6], neural networks [7; 8; 9], fuzzy recognition [10; 11], and traditional decision tree methods [12; 13; 14] to classify and identify complex carbonate rock lithology and carbonate rock diagenetic facies. These methods demonstrate advantages in handling large amounts of data and recognizing complex patterns, which helps improve the accuracy and efficiency of lithology identification [15]. Therefore, combining traditional logging interpretation methods with machine learning techniques can more effectively identify complex carbonate rock lithology, providing more accurate information for logging reservoir evaluation. [PERSON] et al. (2003) [16] utilized density, resistivity, and gamma-ray logging curves to identify lithology in an operational area by analyzing the corresponding logging curve response characteristics in their study of the Songliao Basin in China. Subsequently, [PERSON] et al. [17], through core analysis, selected optimal logging curves and established crossplots, which greatly enhanced the identification of lithology in the Junggar area. Following this, other scholars have also applied similar methods in their research. For example, [PERSON] et al. [18] observed core samples, conducted classification, and then used logging curve crossplots, ultimately achieving lithology identification.