石油学报 ›› 2002, Vol. 23 ›› Issue (4): 102-106.DOI: 10.7623/syxb200204022

• 石油工程 • 上一篇    

人工神经网络在大庆深井钻头优选中的应用

阎铁1, 刘春天1, 毕雪亮1, 张书瑞2   

  1. 1. 大庆石油学院石油工程系, 黑龙江安达, 151400;
    2. 大庆钻井一公司, 黑龙江大庆, 163411
  • 收稿日期:2001-05-21 修回日期:2001-08-08 出版日期:2002-07-25 发布日期:2010-05-21
  • 作者简介:阎铁,男,1957年2月生,1982年毕业于大庆石油学院钻井工程专业,现为大庆石油学院石油工程系教授,博士生导师.

Application of artificial neural network on optimizing bit type in Daqing deep wells

YAN Tie,et al.   

  1. Daqing Petroleum Institute, Anda 151400, China
  • Received:2001-05-21 Revised:2001-08-08 Online:2002-07-25 Published:2010-05-21

摘要:

针对大庆地区深井地层硬、温度高、倾角大等特点,将对深井钻速影响较大的12个因素作为输入层神经元,建立了人工神经网络优选钻头方法。该方法将钻头优选的定量方法和定性方法相结合,使钻头优选结果更加可靠。针对侏罗系地层高硬度、强研磨性等特点,研究并试验了几种特殊类型的钻头。根据对新型钻头钻井结果及以往大庆深井钻井所用钻头数据的统计分析,建立了大庆地区钻头应用情况数据库。在此基础上,应用人工神经网络法对大庆油田深井使用的钻头进行了优选,在大庆2口深井进行的现场试验表明,钻井速度提高了20%以上。

关键词: 人工神经网络, 机械钻速, 深井钻井, 钻头, 优选, 自适应共振理论

Abstract:

Based on the properties of deep_well drilling in Daqing,such as hard formation,high temperature and big dip,a method for optimizing bit type with artificial neural network is proposed.The factors that affect the bit selection are taken as input neural nods,and the quantitative method is combined with the qualitative method.This new method makes the optimizing results of bit more reliable.Considering the characteristics of high degree of hardness, abrasion and high temperature of Jurassic layer,some special bits were investigated and applied.On the basis of statistical analysis about the bits used in Daqing deep wells and field tests of new type bits,a bit database was developed.The types of bit used in Daqing deep wells were optimized with artificial neural network method.The field tests of two deep wells in Daqing show that the drilling rates using optimized bits are raised more than 20%.

Key words: artificial neural network, drilling rate, bit optimization, deep well drilling, adaptive resonance theory

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