Prediction of Annual Increase of Oil Production Based on GM (1, 1)Neural Network Combined Optimization

doi:10.11885/j.issn.1674-5086.2020.06.05.01

Abstract

Abstract: Increasing oil production of old wells has become an inevitable choice to stabilize production and reduce development costs of oilfield block development. In view of the limitation of polynomial regression prediction, the fact that the grey theory cannot reflect the characteristics of influence factors, and the neural network needs more data and is less sensitive to data, this paper establishes an optimal control model, combining the high precision forecasting of grey theory with the neural network. Taking the actual measures to increase oil production in an oilfield block from 2011 to 2018 as an example, by confirming the influence factors of annual oil increment, a new optimal control grey neural network model is established, which is used to predict the annual oil increment with different measures. Compared with polynomial regression prediction, GM(1, 1) prediction and BP neural network prediction, the results show that the new model has better simulation effect and higher prediction precision. The prediction accuracy of the annual oil increment with the new method is 97.34% in 2018. The grey neural network model based on optimal control can be an artificial intelligence model to predict the annual oil increment with different measures, which provides a new idea for accurately predicting of oil increment with different measures and decision-making of oilfield development.

Key words: measure effective well, annual oil increment, grey prediction, BP neural network, optimal control

CLC Number:

TE121.1

LIU Haohan, YAN Yongqin, MIN Lingyuan, YUE Ping, YIN Yanling. Prediction of Annual Increase of Oil Production Based on GM (1, 1)Neural Network Combined Optimization[J]. Journal of Southwest Petroleum University(Science & Technology Edition), 2020, 42(6): 89-96.

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