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

Allou Koffi Franck Kouassi, Lin Pan (), Xiao Wang, Zhangheng Wang, Alvin K. Mulashani, Faulo James, Mbarouk Shaame, Altaf Hussain, Hadi Hussain and Edwin E. Nyakilla
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Allou Koffi Franck Kouassi: Key Laboratory of Theory and Technology of Petroleum Exploration and Development in Hubei Province, China University of Geosciences, Wuhan 430074, China
Lin Pan: Key Laboratory of Theory and Technology of Petroleum Exploration and Development in Hubei Province, China University of Geosciences, Wuhan 430074, China
Xiao Wang: Key Laboratory of Theory and Technology of Petroleum Exploration and Development in Hubei Province, China University of Geosciences, Wuhan 430074, China
Zhangheng Wang: Key Laboratory of Theory and Technology of Petroleum Exploration and Development in Hubei Province, China University of Geosciences, Wuhan 430074, China
Alvin K. Mulashani: Key Laboratory of Theory and Technology of Petroleum Exploration and Development in Hubei Province, China University of Geosciences, Wuhan 430074, China
Faulo James: Key Laboratory of Theory and Technology of Petroleum Exploration and Development in Hubei Province, China University of Geosciences, Wuhan 430074, China
Mbarouk Shaame: Key Laboratory of Theory and Technology of Petroleum Exploration and Development in Hubei Province, China University of Geosciences, Wuhan 430074, China
Altaf Hussain: Key Laboratory of Theory and Technology of Petroleum Exploration and Development in Hubei Province, China University of Geosciences, Wuhan 430074, China
Hadi Hussain: Key Laboratory of Theory and Technology of Petroleum Exploration and Development in Hubei Province, China University of Geosciences, Wuhan 430074, China
Edwin E. Nyakilla: Key Laboratory of Theory and Technology of Petroleum Exploration and Development in Hubei Province, China University of Geosciences, Wuhan 430074, China

Energies, 2023, vol. 16, issue 2, 1-26

Abstract: The precise characterization of geological bodies in fracture-vuggy carbonates is challenging 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 worldwide.

Keywords: fracture-vuggy carbonate reservoir; karst cavities; gradient-boosting decision trees (GBDT); Visual Geometry Group 16 pre-trained (VGG-16); uncertainty; Tahe oilfield (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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