LAPSE:2023.24512
Published Article
LAPSE:2023.24512
A Data-Driven Approach for Lithology Identification Based on Parameter-Optimized Ensemble Learning
Zhixue Sun, Baosheng Jiang, Xiangling Li, Jikang Li, Kang Xiao
March 28, 2023
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.
Keywords
Bayesian Optimization, Extreme Gradient Boosting, formation lithology identification
Suggested Citation
Sun Z, Jiang B, Li X, Li J, Xiao K. A Data-Driven Approach for Lithology Identification Based on Parameter-Optimized Ensemble Learning. (2023). LAPSE:2023.24512
Author Affiliations
Sun Z: School of Petroleum Engineering, China University of Petroleum (East China), Qingdao 266580, China
Jiang B: School of Petroleum Engineering, China University of Petroleum (East China), Qingdao 266580, China [ORCID]
Li X: PetroChina Research Institute of Petroleum Exploration & Development, Beijing 100083, China
Li J: School of Petroleum Engineering, China University of Petroleum (East China), Qingdao 266580, China
Xiao K: PetroChina Research Institute of Petroleum Exploration & Development, Beijing 100083, China
Journal Name
Energies
Volume
13
Issue
15
Article Number
E3903
Year
2020
Publication Date
2020-07-30
Published Version
ISSN
1996-1073
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PII: en13153903, Publication Type: Journal Article
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doi:10.3390/en13153903
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Mar 28, 2023
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