Research Progress of Oilfield Development Index Prediction Based on Artificial Neural Networks

• Main Authors: Chenglong Chen, Yikun Liu, Decai Lin, Guohui Qu, Jiqiang Zhi, Shuang Liang, Fengjiao Wang, Dukui Zheng, Anqi Shen, Lifeng Bo, Shiwei Zhu
• Format: Article
• Language: English
• Published: MDPI AG 2021-09-01
• Series: Energies
• Subjects: neural network; intelligent algorithm; data mining; oilfield development index; prediction model
• Online Access: https://www.mdpi.com/1996-1073/14/18/5844

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.