Logging-data-driven lithology identification in complex reservoirs: an example from the Niuxintuo block of the Liaohe oilfield - Fan, Zuochun; Hu, Changhao; Jiang, Shu; Li, Man; Cai, Ye; Jiang, Yue; Li, Yang; Tian, Mei (Frontiers in Earth Science, 2025)
For lithologic oil reservoirs, lithology identification plays a significant guiding role in exploration targeting, reservoir evaluation, well network adjustment and optimization, and the establishment of reservoir models. Lithology is usually predicted from well log data based on limited core observations. In recent years, machine learning algorithms have been applied to lithology identification to enhance prediction accuracy. In this paper, five algorithms, including Bayes discriminant analysis, Random Forest (RF), Support Vector Machine (SVM), Back Propagation Neural Network (BPNN), and Convolutional Neural Network (CNN) are evaluated for lithology identification using data from the Niuxintuo reservoir. This reservoir is characterized by complex structural and sedimentary features, strong heterogeneity, and intricate lithological properties, all of which present considerable challenges for well logging identification. First, we conducted a detailed observation of the core lithology. Based on the requirements for reservoir evaluation and the principles of logging identification, we reclassify the lithology of the study area into two categories: clastic rocks and dolomite. The clastic rocks are further subdivided into five rock types: fine sandstone, medium-coarse sandstone, conglomerate, mudstone, and transitional rock. The well log series were selected through sensitivity analysis. Then, Bayes discriminant analysis and four machine learning methods were trained to identify the lithology of the study area. The results indicate that except for Bayes discriminant analysis, all the constructed machine learning classifiers demonstrate high prediction accuracy, with the accuracy rate exceeding 85%. Among them, SVM classifier shows the best performance achieving a prediction accuracy as high as 93%. Additionally, the well-trained SVM model was successfully used to predict the lithology profile of blind wells. Our findings provide valuable guidance for predicting the remaining oil distribution and further exploration potential in the Niuxintuo oilfield. Furthermore, this study gains insight into the process and methodology of rapidly predicting lithology of hydrocarbon reservoirs using easily accessible well logging data.