A Data-Driven Approach for Lithology Identification Based on Parameter-Optimized Ensemble Learning

The identification of underground formation lithology can serve as a basis for petroleum exploration and development. This study integrates Extreme Gradient Boosting (XGBoost) with Bayesian Optimization (BO) for formation lithology identification and comprehensively evaluated the performance of the proposed classifier based on the metrics of the confusion matrix, precision, recall, F1-score and the area under the receiver operating characteristic curve (AUC). The data of this study are derived from Daniudui gas field and the Hangjinqi gas field, which includes 2153 samples with known lithology facies class with each sample having seven measured properties (well log curves), and corresponding depth. The results show that BO significantly improves parameter optimization efficiency. The AUC values of the test sets of the two gas fields are 0.968 and 0.987, respectively, indicating that the proposed method has very high generalization performance. Additionally, we compare the proposed algorithm with Gradient Tree Boosting-Differential Evolution (GTB-DE) using the same dataset. The results demonstrated that the average of precision, recall and F1 score of the proposed method are respectively 4.85%, 5.7%, 3.25% greater than GTB-ED. The proposed XGBoost-BO ensemble model can automate the procedure of lithology identification, and it may also be used in the prediction of other reservoir properties.