LAPSE:2023.19488
Published Article
LAPSE:2023.19488
Research Progress of Oilfield Development Index Prediction Based on Artificial Neural Networks
March 9, 2023
Abstract
Accurately predicting oilfield development indicators (such as oil production, liquid production, current formation pressure, water cut, oil production rate, recovery rate, cost, profit, etc.) is to realize the rational and scientific development of oilfields, which is an important basis to ensure the stable production of the oilfield. Due to existing oilfield development index prediction methods being difficult to accurately reflect the complex nonlinear problem in the oil field development process, using the artificial neural network, which can predict the oilfield development index with the function of infinitely close to any non-linear function, will be the most ideal prediction method at present. This article summarizes four commonly used artificial neural networks: the BP neural network, the radial basis neural network, the generalized regression neural network, and the wavelet neural network, and mainly introduces their network structure, function types, calculation process and prediction results. Four kinds of artificial neural networks are optimized through various intelligent algorithms, and the principle and essence of optimization are analyzed. Furthermore, the advantages and disadvantages of the four artificial neural networks are summarized and compared. Finally, based on the application of artificial neural networks in other fields and on existing problems, a future development direction is proposed which can serve as a reference and guide for the research on accurate prediction of oilfield development indicators.
Accurately predicting oilfield development indicators (such as oil production, liquid production, current formation pressure, water cut, oil production rate, recovery rate, cost, profit, etc.) is to realize the rational and scientific development of oilfields, which is an important basis to ensure the stable production of the oilfield. Due to existing oilfield development index prediction methods being difficult to accurately reflect the complex nonlinear problem in the oil field development process, using the artificial neural network, which can predict the oilfield development index with the function of infinitely close to any non-linear function, will be the most ideal prediction method at present. This article summarizes four commonly used artificial neural networks: the BP neural network, the radial basis neural network, the generalized regression neural network, and the wavelet neural network, and mainly introduces their network structure, function types, calculation process and prediction results. Four kinds of artificial neural networks are optimized through various intelligent algorithms, and the principle and essence of optimization are analyzed. Furthermore, the advantages and disadvantages of the four artificial neural networks are summarized and compared. Finally, based on the application of artificial neural networks in other fields and on existing problems, a future development direction is proposed which can serve as a reference and guide for the research on accurate prediction of oilfield development indicators.
Record ID
Keywords
data mining, intelligent algorithm, neural network, oilfield development index, prediction model
Suggested Citation
Chen C, Liu Y, Lin D, Qu G, Zhi J, Liang S, Wang F, Zheng D, Shen A, Bo L, Zhu S. Research Progress of Oilfield Development Index Prediction Based on Artificial Neural Networks. (2023). LAPSE:2023.19488
Author Affiliations
Chen C: College of Petroleum Engineering, Northeast Petroleum University, Daqing 163000, China; Key Laboratory of Improving Oil and Gas Recovery, Ministry of Education, Northeast Petroleum University, Daqing 163000, China [ORCID]
Liu Y: College of Petroleum Engineering, Northeast Petroleum University, Daqing 163000, China; Key Laboratory of Improving Oil and Gas Recovery, Ministry of Education, Northeast Petroleum University, Daqing 163000, China
Lin D: School of Energy Science and Engineering, University of Science and Technology of China, Hefei 230000, China; Guangzhou Institute of Energy Conversion, Chinese Academy of Sciences, Guangzhou 510000, China
Qu G: College of Petroleum Engineering, Northeast Petroleum University, Daqing 163000, China; Key Laboratory of Improving Oil and Gas Recovery, Ministry of Education, Northeast Petroleum University, Daqing 163000, China
Zhi J: College of Petroleum Engineering, Northeast Petroleum University, Daqing 163000, China; Key Laboratory of Improving Oil and Gas Recovery, Ministry of Education, Northeast Petroleum University, Daqing 163000, China
Liang S: College of Petroleum Engineering, Northeast Petroleum University, Daqing 163000, China; Key Laboratory of Improving Oil and Gas Recovery, Ministry of Education, Northeast Petroleum University, Daqing 163000, China [ORCID]
Wang F: College of Petroleum Engineering, Northeast Petroleum University, Daqing 163000, China; Key Laboratory of Improving Oil and Gas Recovery, Ministry of Education, Northeast Petroleum University, Daqing 163000, China [ORCID]
Zheng D: School of Petroleum Engineering, Yangtze University, Wuhan 430000, China
Shen A: College of Petroleum Engineering, Northeast Petroleum University, Daqing 163000, China; Key Laboratory of Improving Oil and Gas Recovery, Ministry of Education, Northeast Petroleum University, Daqing 163000, China; Petroleum Engineering Department, Univer [ORCID]
Bo L: College of Petroleum Engineering, Northeast Petroleum University, Daqing 163000, China; Key Laboratory of Improving Oil and Gas Recovery, Ministry of Education, Northeast Petroleum University, Daqing 163000, China
Zhu S: College of Petroleum Engineering, Northeast Petroleum University, Daqing 163000, China; Key Laboratory of Improving Oil and Gas Recovery, Ministry of Education, Northeast Petroleum University, Daqing 163000, China
Liu Y: College of Petroleum Engineering, Northeast Petroleum University, Daqing 163000, China; Key Laboratory of Improving Oil and Gas Recovery, Ministry of Education, Northeast Petroleum University, Daqing 163000, China
Lin D: School of Energy Science and Engineering, University of Science and Technology of China, Hefei 230000, China; Guangzhou Institute of Energy Conversion, Chinese Academy of Sciences, Guangzhou 510000, China
Qu G: College of Petroleum Engineering, Northeast Petroleum University, Daqing 163000, China; Key Laboratory of Improving Oil and Gas Recovery, Ministry of Education, Northeast Petroleum University, Daqing 163000, China
Zhi J: College of Petroleum Engineering, Northeast Petroleum University, Daqing 163000, China; Key Laboratory of Improving Oil and Gas Recovery, Ministry of Education, Northeast Petroleum University, Daqing 163000, China
Liang S: College of Petroleum Engineering, Northeast Petroleum University, Daqing 163000, China; Key Laboratory of Improving Oil and Gas Recovery, Ministry of Education, Northeast Petroleum University, Daqing 163000, China [ORCID]
Wang F: College of Petroleum Engineering, Northeast Petroleum University, Daqing 163000, China; Key Laboratory of Improving Oil and Gas Recovery, Ministry of Education, Northeast Petroleum University, Daqing 163000, China [ORCID]
Zheng D: School of Petroleum Engineering, Yangtze University, Wuhan 430000, China
Shen A: College of Petroleum Engineering, Northeast Petroleum University, Daqing 163000, China; Key Laboratory of Improving Oil and Gas Recovery, Ministry of Education, Northeast Petroleum University, Daqing 163000, China; Petroleum Engineering Department, Univer [ORCID]
Bo L: College of Petroleum Engineering, Northeast Petroleum University, Daqing 163000, China; Key Laboratory of Improving Oil and Gas Recovery, Ministry of Education, Northeast Petroleum University, Daqing 163000, China
Zhu S: College of Petroleum Engineering, Northeast Petroleum University, Daqing 163000, China; Key Laboratory of Improving Oil and Gas Recovery, Ministry of Education, Northeast Petroleum University, Daqing 163000, China
Journal Name
Energies
Volume
14
Issue
18
First Page
5844
Year
2021
Publication Date
2021-09-15
ISSN
1996-1073
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PII: en14185844, Publication Type: Review
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LAPSE:2023.19488
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https://doi.org/10.3390/en14185844
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