MACHINE LEARNING LITHOLOGY PREDICTION USING WIRELINE LOGS
This paper provides a complete model for predicting lithology using well log data. The model is based on an ensemble of binary classifiers that are based on the popular extreme gradient boosting (XGBoost) algorithm. The article also proposes a way to fill in missing log data to improve the forecast accuracy based on regression extreme gradient boosting (XGBoost regressor). To estimate the accuracy of the algorithm, the F-score was used, as well as a metric that estimates the degree of "error" of the result. The accuracy of the algorithm according to the F-score is 0.64, and according to the second metric - 0.612.