Application of convolutional long short-term memory for spatio-temporal forecastings of hydrocarbon saturations and pressure in oil fields-Reference-Cited by-同舟云学术

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Application of convolutional long short-term memory for spatio-temporal forecastings of hydrocarbon saturations and pressure in oil fields

Published:2024-09-11 Issue: Volume: Page:1-25
ISSN:1091-6466
Container-title:Petroleum Science and Technology
language:en
Short-container-title:Petroleum Science and Technology

Author:

Panja Palash¹²³^ORCID,Jia Wei¹³⁴,Nelson Alec¹⁴,McPherson Brian¹³⁴

Affiliation:

1. National Energy Technology Laboratory, Pittsburgh, Pennsylvania, USA

2. Department of Chemical Engineering, University of Utah, Salt Lake City, Utah, USA

3. Energy & Geoscience Institute, University of Utah, Salt Lake City, Utah, USA

4. Department of Civil and Environmental Engineering, University of Utah, Salt Lake City, Utah, USA

Funder

National Energy Technology Laboratory

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/10916466.2024.2400303

Reference29 articles.

1. Anaconda Navigator. https://docs.anaconda.com/anaconda/navigator/ Accessed 2021. https://anaconda.org/.

2. Interpretable spatio-temporal attention LSTM model for flood forecasting

3. Dozat T. 2016. Incorporating nesterov momentum into adam.

4. Machine Learning as a Reliable Technology for Evaluating Time-Rate Performance of Unconventional Wells

5. Long Short-Term Memory

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