基于粒子群优化算法的小波神经网络缝洞型储层识别模型
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摘要
针对缝洞型储层识别精度较低这一难题,提出了基于粒子群优化算法的小波神经网络(PSO-WNN)储层识别模型。以小波函数作为隐含层的激励函数,采用粒子群优化算法,对权值、伸缩参数、平移参数进行调整,构建出基于粒子群优化算法的小波神经网络储层识别模型。该模型具有算法简单、结构稳定、计算收敛速度快、全局寻优能力强、识别精度高、泛化能力强的优点。这里以济阳坳陷桩西埕岛地区古生界潜山缝洞型储层识别为例,利用常规测井参数作为模型的输入参数,以储层类型赋值作为输出,选取九口井的108个已知样本,采用不同隐含层个数对模型进行多次训练。通过对比分析,最终确定隐含层个数为10,建立起该区的Ⅰ类、Ⅱ类、Ⅲ类储层识别模型。利用已建模型对十八个检验样本进行识别,其识别正确率高达100%,而BP神经网络识别正确率为88%。这表明该模型对缝洞型储层的识别效果较好,为缝洞型储层的进一步研究提供了可靠的依据。
It is difficult to accurately identify reservoir, this paper proposes a model of wavelet neural networks based on PSO (PSO-WNN) to solve the problem.The activation function which is in the hidden layer is wavelet function.The fracture-caves reservoir identification model is formed based on PSO-based Wavelet Neural Network, and PSO is used to adjust the parameters of weight, stretching, parallel move.The model has the advantage of simple algorithm, structural stability,fast speed of convergence,global optimization ability,high-accuracy of identification,generalization ability,etc.108 samples of 9 testing wells from the reservoir of buried-hill in Zhuangxi-Chengdao,Jiyang sag were selected, and the input parameters are conventional logging parameters and the output parameter is the assignment of the type of reservoir.In the identification,different number of hidden layer has been used to train the model and the final number of hidden layer is finally decided as 10 through comparative analysis.The model of Ⅰ,Ⅱ, Ⅲ type of reservoir is established. In the 18 samples for check, the correction rate is 100% while BP is 88%,which shows that the model is better for fracture-caves reservoir. The method is providing a reliable basis for the further study of fracture-caves reservoir.
It is difficult to accurately identify reservoir, this paper proposes a model of wavelet neural networks based on PSO (PSO-WNN) to solve the problem.The activation function which is in the hidden layer is wavelet function.The fracture-caves reservoir identification model is formed based on PSO-based Wavelet Neural Network, and PSO is used to adjust the parameters of weight, stretching, parallel move.The model has the advantage of simple algorithm, structural stability,fast speed of convergence,global optimization ability,high-accuracy of identification,generalization ability,etc.108 samples of 9 testing wells from the reservoir of buried-hill in Zhuangxi-Chengdao,Jiyang sag were selected, and the input parameters are conventional logging parameters and the output parameter is the assignment of the type of reservoir.In the identification,different number of hidden layer has been used to train the model and the final number of hidden layer is finally decided as 10 through comparative analysis.The model of Ⅰ,Ⅱ, Ⅲ type of reservoir is established. In the 18 samples for check, the correction rate is 100% while BP is 88%,which shows that the model is better for fracture-caves reservoir. The method is providing a reliable basis for the further study of fracture-caves reservoir.
引文
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[15]张章华,孟凡卉,王文娟.基于粒群算法的神经网络在岩性识别上的应用[J].物探化探计算技术,2007,29(2):130
[2]李银国,张邦礼,营长修.小波神经网络及其结构设计方法[J].模式识别与人工智能,1997,10(3):197.
[3]王文娟,曹俊兴,张元标,等.基于微粒群算法的神经网络储层物性参数预测[J].西南石油大学学报,2007,29(6):31.
[4]ZHANG Q,BEAVENISTS A.Wavelet Networks[J].IEEE Trans on Neural Networks,1992,3(6):889.
[5]DAUBECHIES.The Wavelet Trandform.Time-frequen-cy Localization and Signal Analysis[J].IEEE Trans OnInformation Theory,1990,36(5):226.
[6]KENNEDY J,EBERHARTR C.Particle Swarm Optimi-zation[C].In Proceeding of IEEE International Confer-ence on Neural Networks,Ⅳ.Perth,1995:1942.
[7]张银德,匡建超,曾剑毅.基于粒子群算法的模糊优选神经网络储层识别模型[J].物探化探计算技术,2008,30(3):202.
[8]周文.裂缝性油气储集层评价方法[M].成都:四川科学技术出版社,1998.
[9]袁海峰,徐国盛,王威.济阳坳陷桩西埕岛地区古潜山下古生界储层的综合判识及分布规律[J].成都理工大学学报(自然科学版),2005,32(1):27.
[10]黄科,肖慈,张梢楠,等.基于神经网络的储层识别处理技术及在塔河油田的应用[J].物探化探计算技术.2006,28(1):18.
[11]雍世和.裂缝性储集层的识别与评价[J].测井技术.1985,9(4):82.
[12]匡建超,陈小花.新场气田开发方案寻优的遗传优化神经网络模型[J].物探化探计算技术,2008,30(4):288.
[13]张慧珍,王山山,马良.地震多参数神经网络储层油气预测中的参数优化[J].物探化探计算技术,2007,29(5):420.
[14]王铮.基于重置神经网络在油气储层预测方面的应用[J].物探化探计算技术,2007,29(3):249.
[15]张章华,孟凡卉,王文娟.基于粒群算法的神经网络在岩性识别上的应用[J].物探化探计算技术,2007,29(2):130
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