应用科学学报 ›› 2018, Vol. 36 ›› Issue (4): 689-697.doi: 10.3969/j.issn.0255-8297.2018.04.012

• 计算机科学与应用 • 上一篇    下一篇

DBN在测井解释中的研究与应用

段友祥, 徐冬胜, 孙歧峰, 李钰   

  1. 中国石油大学(华东)计算机与通信工程学院, 山东 青岛 266580
  • 收稿日期:2017-08-04 修回日期:2017-10-05 出版日期:2018-07-31 发布日期:2018-07-31
  • 通信作者: 段友祥,教授,研究方向:网络与服务计算、计算机技术在油气领域的应用等,E-mail:yxduan@upc.edu.cn E-mail:yxduan@upc.edu.cn
  • 基金资助:
    十三·五“重大专项”基金(No.2017ZX05009-001)资助

Research and Application on DBN for Well Log Interpretation

DUAN You-xiang, XU Dong-sheng, SUN Qi-feng, LI Yu   

  1. College of Computer and Communication Engineering, China University of Petroleum(East China), Qingdao 266580, China
  • Received:2017-08-04 Revised:2017-10-05 Online:2018-07-31 Published:2018-07-31

摘要: 测井解释就是将测井信息加工解释成地质信息,以往大多通过数学方法建立解释模型或者使用最基本的反向传播(back propagation,BP)网络来完成这项工作.针对BP网络存在学习训练效率不高的问题,提出了将深度置信网络应用于测井曲线解释.针对测井解释的特点,选择4条测井曲线数据作为输入进行泥砂分层以及孔隙度的预测实验,并与BP网络的预测结果进行对比分析.实验表明,深度置信网络可用于测井曲线解释,其分类精度较一般BP算法有所提高并且训练时间有所降低.

关键词: 人工神经网络, 深度置信网络, 测井解释, 泥砂分层, 储层参数预测

Abstract: Well log interpretation refers to interpreting logging information into geological information, which was generally accomplished by establishing mathematical models or using the fundamental BP networks in the past. This study proposes to apply the deep belief network (DBN) to the interpretation of logging curve. We used four well log curves as input parameters, conducted the mudstone, and conducted the sandstone layering experiment and reservoir parameter prediction experiment with the DBN method. The results of experiment show that the DBN performs well in the interpretation of logging curve, with higher classification accuracy and shorter training time than that of BP algorithm.

Key words: artificial neural network, well log interpretation, prediction of reservoir parameters, deep belief network (DBN), mudstone and sandstone layering

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