研究论文
Characterization of lacustrine shale oil reservoirs based on a hybrid deep learning model: A data-driven approach to predict lithofacies, vitrinite reflectance, and TOC
Bo Liu, Y. Ma, Qamar Yasin, David A. Wood, Mengdi Sun, Shuo Gao, Yunfeng Bai
Northeast Petroleum University Daqing Oilfield of CNPC
来源Marine and Petroleum Geology
年份2025
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学术脉络
学科主题
工程Hydrocarbon exploration and reservoir analysis
Geochemistry and Geologic Mapping · Machine Learning in Materials Science
参考文献 93
A Practical Guide to Training Restricted Boltzmann Machines
被引 2,851Geoffrey E. Hinton · Lecture notes in computer science · 2012
Greedy function approximation: A gradient boosting machine.
被引 30,381Jerome H. Friedman · The Annals of Statistics · 2001
LSTM: A Search Space Odyssey
被引 6,946Klaus Greff, Rupesh K. Srivastava, Jan Koutník · IEEE Transactions on Neural Networks and Learning Systems · 2016
此处列出前 3 条
引用本文 17
Review of Machine Learning Methods for Steady State Capacity and Transient Production Forecasting in Oil and Gas Reservoir
被引 24Dongyan Fan, Sicen Lai, Hai Xia Sun · Energies · 2025
Swin-UNETR: A transformer-based model for 3D pore network segmentation in low-permeability sedimentary rocks
被引 20Mengdi Sun, Qamar Yasin, Eshimiakhe Daniel · International Journal of Coal Geology · 2026
Digital core modeling for multimineral segmentation of lacustrine shale oil using FE-SEM and KiU-Net
被引 11Qamar Yasin, Bo Liu, Mengdi Sun · Fuel · 2025
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