A comparative machine learning study for time series oil production forecasting: ARIMA, LSTM, and Prophet

It is challenging to predict the production performance of unconventional reservoirs because of the sediment heterogeneity, intricate flow channels, and complex fluid phase behavior. The traditional oil production prediction methods (e.g., decline curve analysis and reservoir simulation modeling for...

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Bibliographic Details
Published in:	Computers & geosciences Vol. 164; p. 105126
Main Authors:	Ning, Yanrui, Kazemi, Hossein, Tahmasebi, Pejman
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 01.07.2022
Subjects:	ARIMA artificial intelligence basins LSTM Machine learning neural networks Oil production prediction oils prediction sediments time series analysis Time series forecasting Time series forecasting Oil production prediction LSTM ARIMA Machine learning
ISSN:	0098-3004
Online Access:	Get full text
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