基于RDT&FDF方法的石油钻井事故诊断系统
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摘要
本文在深入探讨当前国内石油钻井工程事故诊断预报领域现状的基础上,分析了我国目前钻井事故诊断中存在的问题,并归纳的两点主要原因：第一、钻井传感器信号异常的捕获不够准确及时,导致井下事故时体现在传感器信号变化被忽略,无法做出相应的事故预报。第二、钻井事故诊断的方法盲目借鉴国外钻井事故诊断的方法,忽略了我国油气存储的地质的特殊性以及我国目前的钻井装备技术水平,当井下事故发生时,不能够做出及时准确的预报。
针对我国钻井事故诊断中存在的这两个难点,本文提出了基于RDT (Related Dynamic Threshold,自相关动态门限)&FDF (Fuzzy Data Fusion,模糊数据融合)方法的钻井事故诊断系统的研究。本文的主要工作如下：
首先,介绍了各个钻井相关传感器在钻井作业中的作用,以及他们所体现的钻井工程相关物理量的意义。之后介绍了钻井事故的成因、危害以及钻井事故发生时体现在传感器上的特征表现。
其次,提出了一种新的基于自相关动态门限的钻井传感器信号异常检测的方法,提高了对钻井传感器信号异常的捕获能力。同时,对比了D-S证据理论、神经网络和灰色关联理论方法在钻井事故诊断中的应用,针对上述方法的不足提出了基于模糊数据融合的钻井故障诊断方法,更加贴近钻井作业模糊性和不确定性强的特点。
最后,介绍了整体系统的实现,钻井事故诊断系统作为整套油井服务系统三大子系统之一,由数据处理模块,和诊断模块两大部分组成,介绍了自定义数据字典的格式以及钻井重要参数的计算推导和事故诊断中间参数的配置方法。给出了系统的运行效果。
经测试系统捕获传感器信号异常的能力比较强,且可以实现随钻井作业的进度和当前钻进岩性的不同动态调整传感器门限。钻井事故的诊断结合人工确认的方法,能够确保诊断依据的正确性,较好地将人工经验与智能诊断相结合。
Base on the deep research of present situation on domestic oil drilling engineering accident diagnosis this paper has analyzed the problems in this field. There are two main reasons lead to the not so good drilling accident diagnosis effect:Firstly, the catch of sensor signal abnormalities is not accurate or in time so that lead to the system would neglect the change of the sensor signal abnormalities. Secondly, many researchers use foreign advanced method in the drilling engineering accident diagnosis and forecast in spite of whether or not fit to domestic geological particularity technology level. When some accident happened in the well, these methods can not make an accurate forecast.
To deal with the two problems above, a new drilling engineering accident diagnosis and forecast method based on based on RDT&FDF (Related Dynamic Threshold and Fuzzy Data Fusion) is proposed in this paper. Main tasks in this paper are as follows:
Firstly, this paper introduces the all kinds of sensors in the drilling process and their effect. And then the reason and danger of drilling accident are expounded in the next paragraph. In the end of the first part this paper states the sensor reflections when the accident happened in the well.
Secondly, this paper rise up the accuracy on the catch of sensor signal abnormalities while using the related dynamic threshold method. At the same time, contrast with application of D-S Evidence theory, neural net and gray theory in the drilling accident diagnosis, the method based on fuzzy data fusion is much more close to the characteristic fuzziness and uncertainty of the drilling process.
At last, the petroleum drilling accident forecast system contains two main parts:data processing module and diagnosis module. In this part, database design, drilling parameters calculation process and middle parameters of the accident diagnosis is introduced and the result is given in the last part of this paper.
After testing the new method has a good capacity to catch of sensor signal abnormalities and can change the threshold of the sensor in the different drilling stage or in the different rock stratum. Meanwhile, this method combines the artificial experience with the software accident diagnosis to ensure the accuracy of the accident forecast result.
针对我国钻井事故诊断中存在的这两个难点,本文提出了基于RDT (Related Dynamic Threshold,自相关动态门限)&FDF (Fuzzy Data Fusion,模糊数据融合)方法的钻井事故诊断系统的研究。本文的主要工作如下：
首先,介绍了各个钻井相关传感器在钻井作业中的作用,以及他们所体现的钻井工程相关物理量的意义。之后介绍了钻井事故的成因、危害以及钻井事故发生时体现在传感器上的特征表现。
其次,提出了一种新的基于自相关动态门限的钻井传感器信号异常检测的方法,提高了对钻井传感器信号异常的捕获能力。同时,对比了D-S证据理论、神经网络和灰色关联理论方法在钻井事故诊断中的应用,针对上述方法的不足提出了基于模糊数据融合的钻井故障诊断方法,更加贴近钻井作业模糊性和不确定性强的特点。
最后,介绍了整体系统的实现,钻井事故诊断系统作为整套油井服务系统三大子系统之一,由数据处理模块,和诊断模块两大部分组成,介绍了自定义数据字典的格式以及钻井重要参数的计算推导和事故诊断中间参数的配置方法。给出了系统的运行效果。
经测试系统捕获传感器信号异常的能力比较强,且可以实现随钻井作业的进度和当前钻进岩性的不同动态调整传感器门限。钻井事故的诊断结合人工确认的方法,能够确保诊断依据的正确性,较好地将人工经验与智能诊断相结合。
Base on the deep research of present situation on domestic oil drilling engineering accident diagnosis this paper has analyzed the problems in this field. There are two main reasons lead to the not so good drilling accident diagnosis effect:Firstly, the catch of sensor signal abnormalities is not accurate or in time so that lead to the system would neglect the change of the sensor signal abnormalities. Secondly, many researchers use foreign advanced method in the drilling engineering accident diagnosis and forecast in spite of whether or not fit to domestic geological particularity technology level. When some accident happened in the well, these methods can not make an accurate forecast.
To deal with the two problems above, a new drilling engineering accident diagnosis and forecast method based on based on RDT&FDF (Related Dynamic Threshold and Fuzzy Data Fusion) is proposed in this paper. Main tasks in this paper are as follows:
Firstly, this paper introduces the all kinds of sensors in the drilling process and their effect. And then the reason and danger of drilling accident are expounded in the next paragraph. In the end of the first part this paper states the sensor reflections when the accident happened in the well.
Secondly, this paper rise up the accuracy on the catch of sensor signal abnormalities while using the related dynamic threshold method. At the same time, contrast with application of D-S Evidence theory, neural net and gray theory in the drilling accident diagnosis, the method based on fuzzy data fusion is much more close to the characteristic fuzziness and uncertainty of the drilling process.
At last, the petroleum drilling accident forecast system contains two main parts:data processing module and diagnosis module. In this part, database design, drilling parameters calculation process and middle parameters of the accident diagnosis is introduced and the result is given in the last part of this paper.
After testing the new method has a good capacity to catch of sensor signal abnormalities and can change the threshold of the sensor in the different drilling stage or in the different rock stratum. Meanwhile, this method combines the artificial experience with the software accident diagnosis to ensure the accuracy of the accident forecast result.
引文
[1]李诚铭.新编石油钻井工程技术手册[M].北京：中国知识出版社.2006.
[2]周开吉,郝俊芳.钻进工程设计[M].山东：石油大学出版社.1996.
[3]蒋希文.钻井事故与复杂问题[M].北京：石油工业出版社.2001.
[4]孙中昌,李富强.钻井异常预测技术[M].北京：石油工业出版社,2006.
[5]訾艳阳.基于非平稳信号特征提取原理的实用诊断技术研究[D].西安：西安交通大学,2000.
[6]杜伯学,陆宇航,张强.基于传感器阈值法的局部放电信号自消噪[J].高电压技术,2008,34(8)：1578-1582.
[7]徐玉秀,原培新,杨文平.复杂机械故障诊断的分形与小波方法[M].北京：机械工业出版社,2003：15-21.
[8]李凤霞,崔茂荣,王丽华,等.综合录井技术在实时监测钻井事故中的应用.断块油气田,2007.14(3).66-68.
[9]刘瑞文,郭学增.钻井过程中的在线故障检测与诊断[J].石油钻探技术.2004.32(4)84-86.
[10]王向辉.基于传感器故障检测的阈值选取原则的研究[J].机械工程与自动化.2009(2)：105-106.
[11]Lee S. K., White P. R., The enhancement of impulsive noise and vibration signals for fault detection in rotating and reciprocating machinery [J]. Journal of Sound and Vibration, 1998,217(3):485-505.
[12]王江萍,李飞,鲍泽富.多传感器信息融合方法在钻井事故诊断中的应用[J].新技术新工艺,2009,2：61-63.
[13]吴明强.故障诊断专家系统研究的现状与展望[J].计算机测量与控制,2005,13(12)：2-5.
[14]李海港,周一恒.神经网络故障诊断专家系统的结构设计[J].煤矿机械,2005,1(2)：127-128.
[15]韩性礼.神经网络技术在钻井工程事故预测、预报中的应用[J].录井工程,2007,18(1)：28-32.
[16]刘瑞文,郭学增.钻井过程中在线故障识别的灰色关联分析法[J].钻井工艺,2004,27(6)：17-25.
[17]廖明燕.基于神经网络和证据理论集成的钻井过程状态检测与故障诊断[J].中国石油大学学报(自然科学版),2007,31(5)：136-140.
[18]孙超.设备故障特征提取及其隶属函数建立关键技术研究[D].河南：郑州大学机械工程学院,2004.
[19]刘金华.工程参数录井在钻井工程中的应用[J].断块油气田,2003,9(1)：87-89.
[20]韩朝辉,郭建明,高晓荣.钻井事故人工智能监控系统[J].电气传动自动化,2007,29(4)：52-53.
[21]杨松林.工程模糊数学论方法及其应用[M].国防工业出版社.1996.
[22]朱晓东,王杰.基于分层模糊系统的石油钻井参数预测模型[J].石油学报.2010.30(5)839-842.
[23]李伟,周先文.综合录井技术在川西地区钻探中的应用[J].天然气工业,2006,26(5)：67-69.
[24]刘瑞文,管志川.利用综合录井信息实时监控钻井过程[J].石油钻探技术,1999,1(1)：24-25.
[25]Fisher, S. Application of Well Logging Techniques to the Marine Sciences. Proceedings of the IR[J].1962,50(11):2243-2251.
[26]Jogi P., Macpherson J, Newbirt M et al. Visualization of BHA Dynamics Improves Understanding of Downhole Drilling Conditions, Speeds Up Learning Curve[R]. IADC/SPE 99181,2006.
[27]Macpherson, J.D., Pogi P.N., Kingman J.E. E. et al. Application and Analysis of Simultaneous Near Bit and Surface Dynamics Measurements[R]. SPE 74718,2006
[28]Jerome J. and Tennyson Dr. Reduce Torsional Vibration and Improve Drilling Operation[R]. SPE 81174,2003.
[29]Reeves M., Macpherson J., Zaeper R. et al. High-Speed Drilling Telemetry Network Enables New Real-Time Drilling and Measurement Technologies[R]. IADC/SPE 99134,2006.
[30]Falconer I.G. Separating bit and lithology effects from drilling mechanics data. IADC/SPE 17 191,1996.
[31]Booer A. K., Meehan R. J. Drillstring imaging:an interpretation of surface drilling vibration. IADC/SPE23 889,1992.
[32]刘光法.开发井小型综合录井仪研制[D].合肥.合肥工业大学,2003.
[33]白耀民.迟到深度计算方法研究及编程实现[J].录井技术,1998,9(4)：57-60.
[34]孙鑫,余安萍,VC++深入详解[M].北京：电子工业出版社,2006：606,561.
[35]陈雪梅.基于ADO技术实现多种数据源间SQL查询功能[J].计算机技术与发展,2007,17(10)：7-11.
[36]刘增军,向为,孙广富.基于ADO的数据库开发技术研究[J].科学技术与工程,2007,7(5)：747-752.
[2]周开吉,郝俊芳.钻进工程设计[M].山东：石油大学出版社.1996.
[3]蒋希文.钻井事故与复杂问题[M].北京：石油工业出版社.2001.
[4]孙中昌,李富强.钻井异常预测技术[M].北京：石油工业出版社,2006.
[5]訾艳阳.基于非平稳信号特征提取原理的实用诊断技术研究[D].西安：西安交通大学,2000.
[6]杜伯学,陆宇航,张强.基于传感器阈值法的局部放电信号自消噪[J].高电压技术,2008,34(8)：1578-1582.
[7]徐玉秀,原培新,杨文平.复杂机械故障诊断的分形与小波方法[M].北京：机械工业出版社,2003：15-21.
[8]李凤霞,崔茂荣,王丽华,等.综合录井技术在实时监测钻井事故中的应用.断块油气田,2007.14(3).66-68.
[9]刘瑞文,郭学增.钻井过程中的在线故障检测与诊断[J].石油钻探技术.2004.32(4)84-86.
[10]王向辉.基于传感器故障检测的阈值选取原则的研究[J].机械工程与自动化.2009(2)：105-106.
[11]Lee S. K., White P. R., The enhancement of impulsive noise and vibration signals for fault detection in rotating and reciprocating machinery [J]. Journal of Sound and Vibration, 1998,217(3):485-505.
[12]王江萍,李飞,鲍泽富.多传感器信息融合方法在钻井事故诊断中的应用[J].新技术新工艺,2009,2：61-63.
[13]吴明强.故障诊断专家系统研究的现状与展望[J].计算机测量与控制,2005,13(12)：2-5.
[14]李海港,周一恒.神经网络故障诊断专家系统的结构设计[J].煤矿机械,2005,1(2)：127-128.
[15]韩性礼.神经网络技术在钻井工程事故预测、预报中的应用[J].录井工程,2007,18(1)：28-32.
[16]刘瑞文,郭学增.钻井过程中在线故障识别的灰色关联分析法[J].钻井工艺,2004,27(6)：17-25.
[17]廖明燕.基于神经网络和证据理论集成的钻井过程状态检测与故障诊断[J].中国石油大学学报(自然科学版),2007,31(5)：136-140.
[18]孙超.设备故障特征提取及其隶属函数建立关键技术研究[D].河南：郑州大学机械工程学院,2004.
[19]刘金华.工程参数录井在钻井工程中的应用[J].断块油气田,2003,9(1)：87-89.
[20]韩朝辉,郭建明,高晓荣.钻井事故人工智能监控系统[J].电气传动自动化,2007,29(4)：52-53.
[21]杨松林.工程模糊数学论方法及其应用[M].国防工业出版社.1996.
[22]朱晓东,王杰.基于分层模糊系统的石油钻井参数预测模型[J].石油学报.2010.30(5)839-842.
[23]李伟,周先文.综合录井技术在川西地区钻探中的应用[J].天然气工业,2006,26(5)：67-69.
[24]刘瑞文,管志川.利用综合录井信息实时监控钻井过程[J].石油钻探技术,1999,1(1)：24-25.
[25]Fisher, S. Application of Well Logging Techniques to the Marine Sciences. Proceedings of the IR[J].1962,50(11):2243-2251.
[26]Jogi P., Macpherson J, Newbirt M et al. Visualization of BHA Dynamics Improves Understanding of Downhole Drilling Conditions, Speeds Up Learning Curve[R]. IADC/SPE 99181,2006.
[27]Macpherson, J.D., Pogi P.N., Kingman J.E. E. et al. Application and Analysis of Simultaneous Near Bit and Surface Dynamics Measurements[R]. SPE 74718,2006
[28]Jerome J. and Tennyson Dr. Reduce Torsional Vibration and Improve Drilling Operation[R]. SPE 81174,2003.
[29]Reeves M., Macpherson J., Zaeper R. et al. High-Speed Drilling Telemetry Network Enables New Real-Time Drilling and Measurement Technologies[R]. IADC/SPE 99134,2006.
[30]Falconer I.G. Separating bit and lithology effects from drilling mechanics data. IADC/SPE 17 191,1996.
[31]Booer A. K., Meehan R. J. Drillstring imaging:an interpretation of surface drilling vibration. IADC/SPE23 889,1992.
[32]刘光法.开发井小型综合录井仪研制[D].合肥.合肥工业大学,2003.
[33]白耀民.迟到深度计算方法研究及编程实现[J].录井技术,1998,9(4)：57-60.
[34]孙鑫,余安萍,VC++深入详解[M].北京：电子工业出版社,2006：606,561.
[35]陈雪梅.基于ADO技术实现多种数据源间SQL查询功能[J].计算机技术与发展,2007,17(10)：7-11.
[36]刘增军,向为,孙广富.基于ADO的数据库开发技术研究[J].科学技术与工程,2007,7(5)：747-752.