XGBoost Formation Thickness Identification Based on Logging Data - Liu, Weikai; Zhao, Yanbin; Yang, Mei; Xu, Yueqing; Li, Guangming; Feng, Ziming (Frontiers in Earth Science, 2022)
Based on research on the response mechanism of formation and reservoir response to logging curves, 12 logging curves were selected in combination with formation depth characteristics, and 4 algorithms were used to identify the formation and reservoir: logistic regression (LR), support vector machine (SVM), random forest (RF), and XGBoost. In the study block, 57 wells out of 60 wells were selected for training and learning, and the remaining three wells were used as prediction samples. The recognition of formation thickness and reservoirs is performed by each of these four machine learning algorithms, and predictive knowledge is obtained separately. It was found that the accuracy of the four algorithms for formation thickness and reservoir layer identification reached over 90%, but the XGBoost algorithm was found to be the best in terms of the four scoring criteria of F1-score, precision, recall, and accuracy. The accuracy of formation thickness identification could reach over 95%, and the correlation analysis between the logging curve and formation thickness could be performed on this basis. The results show that RMN, RLLD, and RLLS have the most obvious response to the sandstone layer, off-surface reservoir, and effective thickness layer, while CAL has the least effect on formation and reservoir identification, which can provide an effective reference for the selection and downscaling of subsequent logging curves.