LM-BP神经网络在泥页岩地层横波波速拟合中的应用
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。...
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| Published in | 中国石油大学学报（自然科学版） Vol. 41; no. 3; pp. 75 - 83 |
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| Main Author | |
| Format | Journal Article |
| Language | Chinese |
| Published |
油气藏地质及开发国家重点实验室,四川成都610059
2017
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%成都理工大学能源学院,四川成都,610059 |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1673-5005 |
| DOI | 10.3969/j.issn.1673-5005.2017.03.009 |
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| Summary: | 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。 |
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| Bibliography: | 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. LU Jing1,2, XIE Runcheng1,2, ZHOU Wen1,2, LIU Yi1,2, YIN Shuai3, ZHANG Chong2（1. State Key Lab of Oil and Gas Reservoir Geology and Exploitation, Chengdu 610059, China;2. School of Energy Resources, Chengdu University of Technology, Chengdu 610059, China;3. School of Energy Resources, China University of Geosciences, Beijing 100083, China） shear wa |
| ISSN: | 1673-5005 |
| DOI: | 10.3969/j.issn.1673-5005.2017.03.009 |