Identification of karst cavities from 2D seismic wave impedance images based on Gradient-Boosting Decision Trees Algorithms (GBDT): Case of Ordovician fracture-vuggy carbonate reservoir, Tahe oilfield, Tarim basin, China

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dc.contributor.authorKouassi, Allou Koffi Franck
dc.contributor.authorPan, Lin
dc.contributor.authorWang, Xiao
dc.contributor.authorWang, Zhangheng
dc.contributor.authorMulashani, Alvin K
dc.contributor.authorJames, Faulo
dc.contributor.authorShaame, Mbarouk
dc.contributor.authorHussain, Altaf
dc.contributor.authorHussain, Hadi
dc.contributor.authorNyakilla, Edwin E
dc.date.accessioned2023-04-01T13:28:41Z
dc.date.available2023-04-01T13:28:41Z
dc.date.issued2023
dc.descriptionFull Text Article. Also available at: https://doi.org/10.3390/en16020643en_US
dc.description.abstractTheprecisecharacterizationofgeologicalbodiesinfracture-vuggycarbonatesischallenging due to their high complexity and heterogeneous distribution. This study aims to present the hybrid of Visual Geometry Group 16 (VGG-16) pre-trained by Gradient-Boosting Decision Tree (GBDT) models as a novel approach for predicting and generating karst cavities with high accuracy on various scales based on uncertainty assessment from a small dataset. Seismic wave impedance images were used as input data. Their manual interpretation was used to build GBDT classifiers for Light Gradient-Boosting Machine (LightGBM) and Unbiased Boosting with Categorical Features (CatBoost) for predicting the karst cavities and unconformities. The results show that the LightGBM was the best GBDT classifier, which performed excellently in karst cavity interpretation, giving an F1-score between 0.87 and 0.94 and a micro-G-Mean ranging from 0.92 to 0.96. Furthermore, the LightGBM performed better in cave prediction than Linear Regression (LR) and Multilayer Perceptron (MLP). The prediction of karst cavities according to the LightGBM model was performed well according to the uncertainty quantification. Therefore, the hybrid VGG16 and GBDT algorithms can be implemented as an improved approach for efficiently identifying geological features within similar reservoirs worldwideen_US
dc.identifier.citationKouassi, A. K. F., Pan, L., Wang, X., Wang, Z., Mulashani, A. K., James, F., ... & Nyakilla, E. E. (2023). Identification of karst cavities from 2D seismic wave impedance images based on Gradient-Boosting Decision Trees Algorithms (GBDT): Case of Ordovician fracture-vuggy carbonate reservoir, Tahe oilfield, Tarim basin, China. Energies, 16(2), 643. https://doi.org/10.3390/en16020643en_US
dc.identifier.otherURL: https://doi.org/10.3390/en16020643
dc.identifier.urihttp://hdl.handle.net/20.500.12661/3677
dc.language.isoenen_US
dc.publisherMDPIen_US
dc.subjectTahe oilfielden_US
dc.subjectTarim basinen_US
dc.subjectKarst cavitiesen_US
dc.subjectGradient-Boosting Decision Trees Algorithmsen_US
dc.subjectGBDTen_US
dc.subject2D Seismic waveen_US
dc.subjectImpedance imagesen_US
dc.subjectFracture-vuggy carbonate reservoiren_US
dc.titleIdentification of karst cavities from 2D seismic wave impedance images based on Gradient-Boosting Decision Trees Algorithms (GBDT): Case of Ordovician fracture-vuggy carbonate reservoir, Tahe oilfield, Tarim basin, Chinaen_US
dc.typeArticleen_US
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