混合学习法前向网络多属性储层参数预测  

RESERVOIR PARAMETER PREDICTION BY MULTI-ATTRIBUTE BASED ON HYBRID LEARNING ALGORITHM OF FEED-FORWARD NETWORK

作　　者：吴秋波[1,2] 吴元 王允诚[1] 

机构地区：[1]成都理工大学能源学院,四川成都610059 [2]中国石油川庆钻探工程有限公司地球物理勘探公司,四川成都610213

出　　处：《西南石油大学学报（自然科学版）》2011年第2期68-72,11,共5页Journal of Southwest Petroleum University(Science & Technology Edition)

摘　　要：应用前向网络描述地震属性和储层参数间的非线性映射关系时,经典的误差反向传播算法存在收敛速度慢,易陷入局部极值等诸多不足。研究了融合粒子群优化算法和误差反向传播算法的混合学习法前向网络多属性储层参数预测技术。粒子群优化算法是一种群体随机搜索演化计算技术,具有较快的收敛速度和较强的全局搜寻能力;误差反向传播算法本质上是梯度下降算法,注重局部搜索。混合学习法为两种学习算法交替执行,首先以粒子群优化算法训练网络,当误差能量在规定的迭代次数内不再发生变化时,采用误差反向传播算法实现局部寻优。理论函数逼近测试和实际储层参数预测实验说明了混合学习法具有学习时间短、求解效率高、可靠性强的优点,具有良好的应用前景。When applying the feed-forward network to describe the nonline arrelationship between seismic attributes and reservoir parameters,the classical error back propagation(BP)learning algorithm has slow convergence and is easy to fall into local minima,and has many other deficiencies.This paper researches a new reservoir parameter prediction technique by seismic multi-attribute which is based on the integration of particle swarm optimization(PSO)algorithm and BPalgorithm of the feed-forward network.PSO is a group randomsearch evolutionary computation technique,has a faster convergence rate and strong global search ability;BP algorithm is essentially a gradient descent algorithm,focusing on local search.Hybrid algorithm trains network by PSO and BP alternatively.That is,when the learning error energy of PSO in required generation has not changed,using BP for local search disturbances.Function approximation test and practical reservoir parameter prediction experiment illustrate the hybrid learning method has a short training time,high efficiency and reliability,and has good prospect.

关 键 词：误差反向传播算法 粒子群优化算法 前向网络 地震属性 孔隙度 

分 类 号：TE132.1[石油与天然气工程—油气勘探] P631.4[天文地球—地质矿产勘探]