基于灰色网络组合优化的年增油量预测

老井措施增油成为油田稳产、降低油田区块开发成本的必然选择。针对多项式回归预测的局限性、灰色理论不能反映影响因素特征、神经网络需求数据多且数据敏感性差等特征，通过建立最优控制模型，实现GM（1，1）灰色理论与神经网络的高精度组合预测。以某油田区块2011-2018年的措施增油为例，对影响措施增油量的因素进行识别，建立了最优控制灰色神经网络模型对老井措施年增油量进行预测，相比多项式回归预测、GM（1，1）预测及BP神经网络预测方法，新模型模拟效果更好，预测精度更高。新方法对2018年措施年增油量的预测精度达97.34%。基于最优控制的灰色神经网络模型可以作为一种人工智能组合最优化模型预测措施年增油量，为准确预测措施增油效果，指导油田开发决策提供了新的思路。

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

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.