Volcanic lithology identification based on parameter-optimized GBDT algorithm: A case study in the Jilin Oilfield, Songliao Basin, NE China

• Published in: Journal of applied geophysics Vol. 194; p. 104443
• Main Authors: Yu, Zhichao, Wang, Zhizhang, Zeng, Fancheng, Song, Peng, Baffour, Bestman Adjei, Wang, Peng, Wang, Weifang, Li, Ling
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.11.2021
• Subjects: Ensemble learning algorithm; GBDT; Lithology identification; Songliao Basin; Volcanic; Ensemble learning algorithm; Volcanic; GBDT; Lithology identification; Songliao Basin
• DOI: 10.1016/j.jappgeo.2021.104443
• ISSN: 0926-9851

The reservoir rocks in the volcanic strata of the Jilin oil field are characterized by great complexity and diversity in composition and structure of lithology. To enhance the rate of lithology identification in subsurface is very laborious. However, lithology identification is often ignored in quantitative studies, though it is the basis for reservoir characterization. In this paper, an ensemble learning algorithm named gradient boosting decision tree (GBDT) was used to establish the classification model for the volcanic lithology identification of the Lower Cretaceous Yingcheng Formation in the Songliao Basin, NE China. At the same time, support vector machine (SVM), logistic regression (LR) and decision tree (DT) classification models were also adopted in contrast with the classification accuracy of GBDT model. Subsequently, the optimal key parameters for each model were determined by employing validation curves and GridSearchCv. These results indicate that the GBDT model is superior to the single classifier and can accurately distinguish the lithologic interface of breccia tuff and rhyolite. Moreover, it also has better recognition ability for thin layer. It was concluded that the ensemble learning algorithm GBDT has significantly enhanced the accuracy of lithology identification and can be used as a lithologic identification technology. •The GBDT model is applied well in volcanic lithology identification.•Parameter optimization is conducted to optimize the classifier.•The GBDT model is superior to the single classifier.•The GBDT model and has better recognition ability for thin layer.