基于遗传变异与BP混合算法的抽油井流入动态预测  

Application of GA and BP algorithm in oil well drifting forecast

作　　者：爨 

机构地区：[1]西安石油大学计算机学院

出　　处：《计算机应用》2004年第8期38-40,53,共4页journal of Computer Applications

基　　金：国家自然科学基金项目 (50 1 750 87) ;陕西省教育厅基金项目 (0 3jk1 62 )

摘　　要：针对BP神经网络中存在的局部极小问题 ,提出了一种基于遗传变异和BP混合的网络算法。在综合考虑了油层渗透率、地层压力、井底流压等对油井产液量产生影响的因素的基础上 ,建立了基于上述网络算法的抽油井流入动态预测模型。实验结果表明 ,该模型具有较高的预测精度 ,预测结果可靠 ,可以作为抽油井流量估计的一种有效手段。In accordance with the local minimum question that exists in BP's neural network, the paper presented a network model based on the features of genetic algorithm and BP network. Considering oil penetrable rate, stratum pressure, liquid pressure, etc, which affect oil well yield, a network model of oil well drifting forecast was established. Experimental results indicate that the method possesses high forecasting precision and reliable calculating result. It can be regarded as an effective method for estimating oil well volume of flow.

关 键 词：遗传算法 神经网络 抽油井流入 动态预测 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]