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

Abstract

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.

Exploratory analysis of machine learning methods in predicting subsurface temperature and geothermal gradient of Northeastern United States

“…In addition, treebased models usually improve the accuracy by decreasing the variance in the prediction (6) (Polikar 2012). XGBoost and Random Forest are both tree-based methods which have been successfully applied in geosciences (Gul et al 2019;Hall 2016;Sun et al 2020). Single decision tree is often referred to as a weak classifier as it can be susceptible to overfitting (Ho 1998).…”

An Ensemble One Dimensional Convolutional Neural Network with Bayesian Optimization for Environmental Sound Classification

“…A perfect model has an AUROC of 1, and random output is indicated by an AUROC of 0.5. The closer AUROC to 1, the better the performance of the model [94].…”

Lithology Logging Recognition Technology Based on GWO-SVM Algorithm

“…erefore, for reservoirs with complex lithology, such as carbonate reservoirs and shale reservoirs, this method is often difficult to apply. Nowadays, many pattern recognition methods are used in lithology recognition: the lithology recognition method based on the gradient boost decision tree [10], the neural network lithology recognition method based on differential evolution based on logging information [11], using the convolutional neural network automatically classifies lithology from drill core images [12], the semisupervised learning method using the Laplace support vector machine for lithology recognition [13], attention-based bidirectional gated recurrent unit neural network lithology identification [14], the lithology identification method based on the recurrent neural network [15], the lithology identification method based on the parameter optimization AdaBoost algorithm [16], the lithology identification method based on the adaptive kernel density Bayesian probability model [17], the lithology identification method based on parameter optimization integrated learning [18], and the logging lithology identification method based on improved multigranularity cascade forest [19,20].…”