Machine Learning Algorithms for Lithofacies Classification of the Gulong Shale from the Songliao Basin, China
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- Timur Merembayev & Darkhan Kurmangaliyev & Bakhbergen Bekbauov & Yerlan Amanbek, 2021. "A Comparison of Machine Learning Algorithms in Predicting Lithofacies: Case Studies from Norway and Kazakhstan," Energies, MDPI, vol. 14(7), pages 1-16, March.
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- Fawz Naim & Ann E. Cook & Joachim Moortgat, 2023. "Estimating Compressional Velocity and Bulk Density Logs in Marine Gas Hydrates Using Machine Learning," Energies, MDPI, vol. 16(23), pages 1-22, November.
- Chao Wang & Chunjing Yan & Zhengjun Zhu & Shaohua Li & Duanchuan Lv & Xixin Wang & Dawang Liu, 2023. "Interpretation of Sand Body Architecture in Complex Fault Block Area of Craton Basin: Case Study of TIII in Sangtamu Area, Tarim Basin," Energies, MDPI, vol. 16(8), pages 1-15, April.
- Radulescu, Magdalena & Dalal, Surjeet & Lilhore, Umesh Kumar & Saimiya, Sarita, 2024. "Optimizing mineral identification for sustainable resource extraction through hybrid deep learning enabled FinTech model," Resources Policy, Elsevier, vol. 89(C).
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Keywords
machine learning models; ensemble methods; XGBoost; random forest; shale lithofacies; well log; Songliao basin; Gulong sag;All these keywords.
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