摘要
一些油藏（例如试井解释）系统的偏微分方程模型，经过变换能化为非线性函数项级数。级数的每一项均为地层参数θ的复杂非线性函数。级数的项数ｎ与模型结构有关，可称为模型的结构参数。把级数中的函数看成非线性神经元，来建立油藏系统的函数型连接人工神经网络模型。用系统辨识理论中的Ｆ检验法确定网络模型的结构参数ｎ，用多步广人梯度学习算法估计网络模型的权系数。地层参数是试井解释的依据，要求其估值应具有唯一性，而上述函数为多峰函数，在极值点处关于θ的变化很敏感，使问题更为困难，现有迭代法和遗传算法均未奏效。一种新型的遗传算法解决了这个问题。应用表明用上述方法建模有很高的精度，能求出地层参数的唯一估值。
Partial differential equation models of some oil reservoir systems,such as well test interpretation could be transformed intoseries composed of non-linear function terms. Every term was a complex non-linear function of stratigraphic parameters θ. Thenumber of term n known as structure parameter of the model was related to model structure. Functions as non-linear neural unitswas viewed to establish function link artificial neural network models of oil reservoir systems. F-test in system identification theory was used to determine the structure parameter n, and multistep generalized gradient learning algorithms to estimate theweighting coefficients of the network. Stratigraphic parameters were the foundation of well test interpretation,their unique estimate values were requested. The problem became more difficult because the above-mentioned aforesaid functions were multimodal functions and very sensitive in extreme point about the change of θ. Neither iteration methods nor genetic algorithms canwork effectively. A new pattern of genetic algorithms was developed to solve the problem. Application has shown that the precision is high. Moreover unique estimate values of stratigraphic parameters were obtained.
出处
《石油学报》
EI
CAS
CSCD
北大核心
1999年第6期53-56,共4页
Acta Petrolei Sinica
基金
黑龙江省自然科学基金
石油天然气集团公司中青年创新基金!97科字第138号
关键词
系统辨识
神经网络
油藏工程
system identification
artificial neural network
test
multi-step generalized gradient method
genetic algorithms
