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铸体薄片图像分析法求取储层孔隙度
引用本文:刘庆利,吴国平,胡剑策,侯卫国.铸体薄片图像分析法求取储层孔隙度[J].测绘学院学报,2009(1).
作者姓名:刘庆利  吴国平  胡剑策  侯卫国
作者单位:中国地质大学信息工程学院;中石化西北分公司;
基金项目:国家自然科学基金资助(40674069)
摘    要:在石油勘探工程中,储层孔隙度是进行油气预测、油气储量计算的重要参数。根据测井铸体薄片图像资料,在传统BP网络模型的基础上提出了一种基于灰色关联约束的BP神经网络改进模型。对铸体薄片图像进行孔隙识别、统计,进而自动判读储层孔隙度。研究和实验表明,该方法对储层孔隙度的分析、判读更具合理性和实用性。

关 键 词:铸体薄片  图像识别  灰色关联约束-BP神经网络  孔隙度判读  

Strike the Reservoir Porosity Using the Casting Sheet Image
LIU Qing-li,WU Guo-ping,HU Jian-ce,HOU Wei-guo.Strike the Reservoir Porosity Using the Casting Sheet Image[J].Journal of Institute of Surveying and Mapping,2009(1).
Authors:LIU Qing-li  WU Guo-ping  HU Jian-ce  HOU Wei-guo
Institution:1.Information Engineering Institute of China University of Geosciences;Wuhan 430074;China;2.China Petroleum & Chemical Corporation of West-north;Wulumuqi 830011;China
Abstract:In the area of the oil exploration engineering,the reservoir porosity is important parameters to forecast and calculate the oil and gas.In terms of the casting sheet images,this paper presented a BP neural networks pattern based on grey relationship,then used it to analysis and identify the pore and to calculate the reservoir porosity automatically.The research and experiments showed that the method was more reasonable and practical to analysis and calculate the reservoir porosity.
Keywords:casting sheet image  image recognition  grey relation restriction-BP neural network  porosity interpretation  
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