中国石油大学学报（自然科学版）2017，Vol.41Issue(3)：75-83,9.DOI:10.3969/j.issn.1673-5005.2017.03.009
LM-BP神经网络在泥页岩地层横波波速拟合中的应用
Application of LM-BP neural network in simulation of shear wave velocity of shale formation
摘要
Abstract
Using elastic wave theory,the parameters such as density,stress,and strain that affect the velocity of P-wave and S-wave are analyzed.The velocities of P-wave and S-wave are tested subsequently in different lithology,saturation state,ambient pressure and axial pressure conditions.Finally,the average relative error is estimated as 2.22％ utilizing the LM-BP neural network fit with experimental results.The results show that the lithology,saturation state and stress state are key factors that influence the relationship of the P-wave and S-wave velocity.To obtain higher accuracy,the LM-BP neural network can be used to fit the S-wave speed under multi-condition.关键词
横波波速/弹性波理论/LM-BP神经网络/测试条件/泥页岩地层Key words
shear wave velocity/elastic wave theory/LM-BP neural network/test condition/shale formation分类
天文与地球科学引用本文复制引用
吕晶,谢润成,周文,刘毅,尹帅,张冲..LM-BP神经网络在泥页岩地层横波波速拟合中的应用[J].中国石油大学学报（自然科学版）,2017,41(3):75-83,9.基金项目
国家自然科学基金项目(41572130) （41572130）
