摘要
将偏最小二乘回归(PLS)与神经网络(NN)耦合,建立了储层参数预报模型。利用偏最小二乘对影响储层参数的诸多因素进行分析,提取对因变量影响强的成分,从而克服了变量间的多重相关性问题,降低了神经网络的输入维数;同时,利用神经网络建模可以较好地解决非线性的储层参数预测问题。计算实例表明,本耦合模型的拟合和预报精度优于独立使用神经网络模型的精度。
This paper proposes a model for predicting the reservoir parameters based on the combination of neural network with the partial least square method. The factors affecting the reservoir parameters are analyzed by means of partial least square method to extract the most important components, so that not only the problem of multi-correlation among variables can be solved but also the amount of input dimensions of the neural network can be reduced. Besides, the application of neural network helps to solve the problem of nonlinearity of the model. The applied example shows that the proposed model has higher precision than those models based on neural network method only.
出处
《成都理工大学学报（自然科学版）》
CAS
CSCD
北大核心
2007年第6期618-620,共3页
Journal of Chengdu University of Technology: Science & Technology Edition
基金
数学地质四川省高校重点实验室资助
国家自然科学基金委员会与中国石油化工股份有限公司联合基金资助项目(40739903)
关键词
偏最小二乘
神经网络
储层参数预测
partial least square method
neural network
reservoir parameter
prediction
