%0 Journal Article
%T LM-BP神经网络在泥页岩地层横波波速拟合中的应用
%A 吕晶
%A 谢润成
%A 周文
%A 刘毅
%A 尹帅
%A 张冲
%J 中国石油大学学报(自然科学版)
%D 2017
%R 10.3969/j.issn.1673-5005.2017.03.009
%X 首先依据弹性波理论对影响纵横波波速的参数进行分析,明确影响横波波速的参数主要包括密度、应力载荷及应变量。根据分析结果,分别测试不同岩性、饱和状态、围压及轴压条件下的岩石纵横波波速。最后以实验结果为最初样本,通过训练LM-BP神经网络,对横波波速实验结果进行拟合,拟合平均相对误差为2.22％。结果表明,岩性、含气性及应力状态是影响纵横波波速主要因素,利用LM-BP神经网络的多条件拟合横波波速具有更高的精度。</br>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
%K 横波波速 弹性波理论 LM-BP神经网络 测试条件 泥页岩地层&lt
%K /br&gt
%K shear wave velocity elastic wave theory LM-BP neural network test condition shale formation
%U http://zkjournal.upc.edu.cn/zgsydxxb/ch/reader/view_abstract.aspx?file_no=20170309&flag=1