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

作　　者：吕晶[1,2] 谢润成[1,2] 周文[1,2] 刘毅[1,2] 尹帅[3] 张冲[2] 

机构地区：[1]油气藏地质及开发国家重点实验室,四川成都610059 [2]成都理工大学能源学院,四川成都610059 [3]中国地质大学能源学院,北京100083

出　　处：《中国石油大学学报（自然科学版）》2017年第3期75-83,共9页Journal of China University of Petroleum(Edition of Natural Science)

基　　金：国家自然科学基金项目(41572130)

摘　　要：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。Using elastic ＂wave theory, the parameters such as density, stress, and strain that affect the velocity of P-wave andS-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-BPneural 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 networkcan 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 

分 类 号：P631.8[天文地球—地质矿产勘探]