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LM-BP神经网络在泥页岩地层横波波速拟合中的应用
吕晶,谢润成,周文,刘毅,尹帅,张冲
0
(油气藏地质及开发国家重点实验室,四川成都 610059;成都理工大学能源学院,四川成都 610059;中国地质大学能源学院, 北京 100083)
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。
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
DOI:10.3969/j.issn.1673-5005.2017.03.009
投稿时间:2016-04-22
基金项目:国家自然科学基金项目(41572130)
Application of LM-BP neural network in simulation of shear wave velocity of shale formation
LÜ Jing,XIE Runcheng,ZHOU Wen,LIU Yi,YIN Shuai,ZHANG Chong
(State Key Lab of Oil and Gas Reservoir Geology and Exploitation, Chengdu 610059, China;School of Energy Resources, Chengdu University of Technology, Chengdu 610059, China;School of Energy Resources, China University of Geosciences, Beijing 100083, China)
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.
Key words:  shear wave velocity  elastic wave theory  LM-BP neural network  test condition  shale formation