测井技术2012，Vol.36Issue(6)：585-589,5.
基于改进SADE算法的神经网络预测储层物性
A New Method Predicting Reservoir Properties with Neural Network Based on SADE Algorithm
摘要
Abstract
order to accurately calculate reservoir properties, the improved Simulated Annealing Differential Evolution Algorithm (SADE) is proposed by combining simulated annaling with differential evolution algorithm. The training of neural network weights in the process of predicting complicated reservoir properties is transformed into an unconstrained optimization problem, and also a new objective function is offered. Then this problem can be solved by SADE algorithm. Compared with other traditional methods, the new objective function is independent of the desired output during the training of neural network, and thus is more suitable for large range of sample data. At the same time, the annealing temperature is used in the algorithm to control the selection process of differential evolution and the differential strategy. In the early stage, the algorithm is of good diversity, while in the late stage, it is of good convergence, overcoming the shortcoming of prematurity in the classical algorithm, and improving the general search ability and robustness. Finally we calculate the field reservoir properties with this algorithm, and obtain good effect.关键词
测井评价/模拟退火/差分进化/神经网络/目标函数/储层物性预测Key words
log evaluation, simulated annealing, differential evolution, neural network, objective function, reservoir properties prediction分类
能源科技引用本文复制引用
李虎,范宜仁,丛云海,胡云云,刘智中..基于改进SADE算法的神经网络预测储层物性[J].测井技术,2012,36(6):585-589,5.基金项目
中国石油天然气集团公司科学研究与技术开发项目(2011D-4101)、中国石油国家重大专项(2011ZX05020-008)、国家自然基金资助项目(41174099)联合资助 （2011D-4101）
