Physics-Assisted Transfer Learning for Production Prediction in Unconventional Reservoirs-Reference-Cited by-同舟云学术

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Physics-Assisted Transfer Learning for Production Prediction in Unconventional Reservoirs

Published:2021 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 9th Unconventional Resources Technology Conference
language:
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Author:

Cornelio J.,Razak S. Mohd,Jahandideh A.,Cho Y.,Liu H-H.,Vaidya R.,Jafarpour B.

Publisher

American Association of Petroleum Geologists

Cited by 8 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Improving the accuracy of short-term multiphase production forecasts in unconventional tight oil reservoirs using contextual Bi-directional long short-term memory;Geoenergy Science and Engineering;2024-04

2. Residual-Enhanced Physics-Guided Machine Learning With Hard Constraints for Subsurface Flow in Reservoir Engineering;IEEE Transactions on Geoscience and Remote Sensing;2024

3. Rapid High-Fidelity Forecasting for Geological Carbon Storage Using Neural Operator and Transfer Learning;Day 1 Mon, October 02, 2023;2023-10-02

4. Physics-Guided Deep Learning for Improved Production Forecasting in Unconventional Reservoirs;SPE Journal;2023-05-18

5. Neural Network-Assisted Clustering for Improved Production Predictions in Unconventional Reservoirs;Day 2 Tue, May 23, 2023;2023-05-15

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