摘要
为解决蒸汽驱开发效果预测精度低和时间长的问题,提出了一种改进人工蜂群算法和RBF(Radial Basis Function)神经网络相融合的预测方法。该方法应用种群最优解修改雇佣蜂解和观察蜂解的搜索方程,借鉴差分进化算法思想,完成对种群最优解和个体搜索解随机扰动,采用混合编码优化RBF神经网络参数。以辽河油田齐40块为例进行了试算,结果表明,该方法对蒸汽驱开发效果预测具有较好的非线性拟合能力和较高的预测精度。
In order to solve the problem of low prediction precision and long prediction time of steam flooding development effect,we propose a novel prediction method,which is based on the combination of improved artificial bee colony algorithm and RBF（Radial Basis Function） neural network.In the proposed method,we apply the optimal solution of the population to modify the search equation of the employed bees and the onlooker bees,perform the random perturbation of the population optimal solution and individual search solution with the idea of differential evolution algorithm,and adopt hybrid encoding to optimize the parameters of RBF neural networks.We use the Qi 40 block of Liaohe Oilfield as an example and make a trial calculation.The trial results show that the method has better nonlinear fitting ability and higher prediction accuracy for steam flooding development effect prediction.
出处
《吉林大学学报（信息科学版）》
CAS
2018年第1期78-84,共7页
Journal of Jilin University（Information Science Edition）
基金
国家科技重大专项课题基金资助项目(2016ZX05012-001)
国家自然科学基金资助项目(61170132)
东北石油大学培育基金资助项目(NEPUPY120224)
关键词
RBF神经网络
人工蜂群算法
随机扰动
蒸汽驱
预测模型
radial basis function （RBF） neural network
artificial bee colony algorithm
random perturbation
steam flooding
prediction model
