Application of Machine Learning for Lithofacies Prediction and Cluster Analysis Approach to Identify Rock Type
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- Vo Thanh, Hung & Lee, Kang-Kun, 2022. "Application of machine learning to predict CO2 trapping performance in deep saline aquifers," Energy, Elsevier, vol. 239(PE).
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- Nafees Ali & Xiaodong Fu & Umar Ashraf & Jian Chen & Hung Vo Thanh & Aqsa Anees & Muhammad Shahid Riaz & Misbah Fida & Muhammad Afaq Hussain & Sadam Hussain & Wakeel Hussain & Awais Ahmed, 2022. "Remote Sensing for Surface Coal Mining and Reclamation Monitoring in the Central Salt Range, Punjab, Pakistan," Sustainability, MDPI, vol. 14(16), pages 1-26, August.
- Ren Jiang & Zhifeng Ji & Wuling Mo & Suhua Wang & Mingjun Zhang & Wei Yin & Zhen Wang & Yaping Lin & Xueke Wang & Umar Ashraf, 2022. "A Novel Method of Deep Learning for Shear Velocity Prediction in a Tight Sandstone Reservoir," Energies, MDPI, vol. 15(19), pages 1-20, September.
- Wakeel Hussain & Muhsan Ehsan & Lin Pan & Xiao Wang & Muhammad Ali & Shahab Ud Din & Hadi Hussain & Ali Jawad & Shuyang Chen & Honggang Liang & Lixia Liang, 2023. "Prospect Evaluation of the Cretaceous Yageliemu Clastic Reservoir Based on Geophysical Log Data: A Case Study from the Yakela Gas Condensate Field, Tarim Basin, China," Energies, MDPI, vol. 16(6), pages 1-25, March.
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Keywords
self-organizing map; cluster analysis; lithofacies; Zamzama gas field; rock type;All these keywords.
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