改进的BP网络新模型在储层损坏预测中的研究
首发时间:2013-05-14
摘要:研究BP神经网络算法,建立一套神经网络预测模型,用于预测评价油气层的油气损害程度,为保护油气层拔高油气产量提供有力帮助。利用MATLAB 2010中的BP神经网络共扼梯度反向算法作为神经网络预测模型,以实验生产井的油气层数据作为训练样本,对该井区油气层进行预测准确率达到了90%。为提高油气的生产和推广神经网络系统在油气预测方面的应用起到了积极促进作用。
For information in English, please click here
The Improved New BP Network Model in Reservoir Damage Prediction Research
Abstract:Research of BP neural network algorithm, the establishment of a set of neural network prediction model for the prediction and evaluation of oil and gas layer of oil and gas the extent of damage and to provide effective help to protect the oil and gas layer overstating oil and gas production. The BP neural network is used in MATLAB 2010 conjugate gradient reverse algorithm as a neural network forecasting model to experimental data of oil and gas production wells layer as training samples, the prediction accuracy rate of 90% of the well area of oil and gas layer. In order to improve oil and gas production and promotion of neural network system has played a positive role in the oil and gas forecasting applications.
Keywords: Algorithm Modeling BP Neural Network Store Corruption Forecast
论文图表:
引用
No.****
同行评议
共计0人参与
勘误表
改进的BP网络新模型在储层损坏预测中的研究
评论
全部评论0/1000