基于数据挖掘技术的深井钻速预测方法研究
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摘要
为了对钻井过程中的机械钻速进行预测,提出了层次分析-神经网络组合模型,将层次分析法确定的钻速影响因素权重和原始数据一并作为输入信息,经过神经网络训练、迭代,确定已钻井钻速模型,最后利用模型对同一区域待钻井钻速进行仿真预测。仿真结果表明,应用层次分析法与前馈神经网络原理建立的AHP-BP组合模型在一定程度上可以提高迭代收敛速度和训练结果的可靠性;利用数据挖掘技术对钻速进行仿真预测,并与实钻值进行对比,可以对钻井状态进行评价,为优选钻进参数、钻头型号及钻井优化提供辅助决策支持。
To predict the rate of penetration(ROP) in the process of drilling,the combined model of analytic hierarchy process(AHP) and neural network was formulated.The weight of the ROP influencing factors determined by the AHP and the original data were taken as the input information.The ROP model of drilled well was determined through network training and iteration.Finally,the model was adopted to make a simulation prediction of the ROP of impending drilling well in the same area.The simulation findings show that the AHP-BP model established by the AHP method and feedforward neural network principle can,to some extent,improve the iterative convergence rate as well as the reliability of the training result.The data mining technology is adopted to make a simulation prediction of ROP which is then compared with the actual drilling value.In this way the drilling state can be assessed and the auxiliary decision support is provided for optimizing drilling parameters and bit types as well as drilling optimization.
To predict the rate of penetration(ROP) in the process of drilling,the combined model of analytic hierarchy process(AHP) and neural network was formulated.The weight of the ROP influencing factors determined by the AHP and the original data were taken as the input information.The ROP model of drilled well was determined through network training and iteration.Finally,the model was adopted to make a simulation prediction of the ROP of impending drilling well in the same area.The simulation findings show that the AHP-BP model established by the AHP method and feedforward neural network principle can,to some extent,improve the iterative convergence rate as well as the reliability of the training result.The data mining technology is adopted to make a simulation prediction of ROP which is then compared with the actual drilling value.In this way the drilling state can be assessed and the auxiliary decision support is provided for optimizing drilling parameters and bit types as well as drilling optimization.
引文
[1]林元华,宗玉宇,梁政,等.石油钻井机械钻速预测研究进展[J].石油钻探技术,2004,32(1):10-13.
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[3]范翔宇,夏宏泉,郑雷清,等.基于地震资料的钻速预测研究[J].大庆石油地质与开发,2007,26(5):121-124.
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[5]彭春耀,焉捷年.人工神经网络在储层敏感性预测中的应用[J].石油钻探技术,1997,25(4):16-18.
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[8]张立明.人工神经网络的模型及其应用[M].上海:复旦大学出版社,1994.
[9]韩家炜,Kamber M.数据挖掘概念与技术[M].2版.北京:机械工业出版社,2007.
[10]Chang Chandong,Zoback M D,Khaksar A.Empiricalrelations between rock strength and physical propertiesin sedimentary rocks[J].Journal of Petroleum Sci-ence&Engineering,2006,51:223-237.
[2]阎铁,毕雪亮,刘春天,等.一种预测深井钻井速度的人工神经网络法[J].石油钻探技术,2001,29(6):9-10.
[3]范翔宇,夏宏泉,郑雷清,等.基于地震资料的钻速预测研究[J].大庆石油地质与开发,2007,26(5):121-124.
[4]Moran D,Ibrahim H,Purwanto A.Sophisticated ROPprediction technologies based on neural network deliversaccurate drill time results[R].IADC/SPE 132010,2010.
[5]彭春耀,焉捷年.人工神经网络在储层敏感性预测中的应用[J].石油钻探技术,1997,25(4):16-18.
[6]罗庆成,徐国新.灰色关联分析与应用[M].南京:江苏科学技术出版社,1986.
[7]阎平凡,张长水.人工神经网络与模拟进化计算[M].北京:清华大学出版社,2000:23-39.
[8]张立明.人工神经网络的模型及其应用[M].上海:复旦大学出版社,1994.
[9]韩家炜,Kamber M.数据挖掘概念与技术[M].2版.北京:机械工业出版社,2007.
[10]Chang Chandong,Zoback M D,Khaksar A.Empiricalrelations between rock strength and physical propertiesin sedimentary rocks[J].Journal of Petroleum Sci-ence&Engineering,2006,51:223-237.
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