基于MWSPCA-CBR的智能预警方法研究及其在石化工业中的应用
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摘要
石油钻井是一项高风险性、耗资巨大的系统工程。为了智能预警石油钻井过程中的异常,缩短非生产时间,降低相关风险,提出一种基于移动窗稀疏主元分析法(MWSPCA)的案例推理(CBR)异常智能预警方法(MWSPCA-CBR)。首先利用MWSPCA算法分析钻井过程中的实时数据,快速定位出异常可能发生的时间,然后使用基于案例推理方法分析异常数据,确定可能的异常类型,并为实时监控专家提供相关异常的处理方法。所提方法应用到石油钻井过程异常预警中,实验结果验证了所提方法的可行性和有效性,为钻井过程降低风险成本提供了新思路。
The petroleum drilling project is a high-risk and costly system project. To effectively scan for potential problems of drilling, reduce non-productive time and lower related risks, this paper proposes an improvedintelligent warning method based on moving window sparse principal component analysis(MWSPCA) integratingcase-based reasoning(CBR)(MWSPCA-CBR). First, the MWSPCA is used to analyze the real-time data in thedrilling process, and the time of occurrence of the anomaly is quickly located. Then the abnormal data is analyzedby using the CBR method to give possible exception types, and the associated handling methods are provided formonitoring experts. Finally, the proposed method is applied to intelligent warn abnormal problems of the petroleumdrilling, the experimental results verify the feasibility and effectiveness of the proposed method and provide new ideas for reducing risks and costs during the petroleum drilling process.
The petroleum drilling project is a high-risk and costly system project. To effectively scan for potential problems of drilling, reduce non-productive time and lower related risks, this paper proposes an improvedintelligent warning method based on moving window sparse principal component analysis(MWSPCA) integratingcase-based reasoning(CBR)(MWSPCA-CBR). First, the MWSPCA is used to analyze the real-time data in thedrilling process, and the time of occurrence of the anomaly is quickly located. Then the abnormal data is analyzedby using the CBR method to give possible exception types, and the associated handling methods are provided formonitoring experts. Finally, the proposed method is applied to intelligent warn abnormal problems of the petroleumdrilling, the experimental results verify the feasibility and effectiveness of the proposed method and provide new ideas for reducing risks and costs during the petroleum drilling process.
引文
[1]张正选,梅大成,高杰,等.钻井工程异常预警方法研究[J].计算机光盘软件与应用, 2014, 17(4):152-152.Zhang Z X, Mei D C, Gao J, et al. Research on abnormity warning method in drilling engineering[J]. Computer CD Software and Applications, 2014, 17(4):152-152.
[2] Gao F, Zhao X H, Liu H S, et al. The study of drilling safe monitoring system based on multi agent group[C]//Advanced Materials Research. Trans. Tech. Publications, 2012, 512:2565-2569.
[3]王林.钻井故障诊断专家系统的研究与实现[D].成都:电子科技大学, 2013.Wang L. The research and implementation of drilling fault diagnosis expert system[D]. Chengdu:University of Electronic Science and Technology of China, 2013.
[4]胡睿.钻井复杂情况与事故诊断专家系统研究[D].青岛:中国石油大学(华东), 2010.Hu R. Research on drilling complex incidents and accident diagnosis based on expert system[D].Qingdao:China University of Petroleum, 2010.
[5]张学洪,李黔.基于案例推理的井漏风险预警方法[J].断块油气田, 2017, 24(2):255-258, 263.Zhang X H, Li Q. Risk pre-warning method of well leakage based on case reasoning[J]. Fault-Block Oil&Gas Field, 2017, 24(2):255-258, 263.
[6]徐术国.基于录井数据的钻井事故预警专家系统[D].北京:中国石油大学(北京), 2011.Xu S G. Drilling accident warning expert system based on logging data[D]. Beijing:China University of Petroleum, 2011.
[7]马鹏鹏,周英操,蒋宏伟,等.钻井风险控制系统关键技术[J].石油钻探技术, 2014, 42(3):16-21.Ma P P, Zhou Y C, Jiang H W, et al. Key technology for drilling risk control system[J]. Petroleum Drilling Techniques, 2014, 42(3):16-21.
[8] Ferreiro S, Sierra B, Irigoien I, et al. Data mining for quality control:Burr detection in the drilling process[J]. Computers&Industrial Engineering, 2011, 60(4):801-810.
[9]袁俊和.钻井过程中的故障诊断方法研究[D].青岛:中国石油大学(华东), 2008.Yuan J H. Study of fault diagnosis methods in well drilling process[D]. Qingdao:China University of Petroleum, 2008.
[10]张东海,席继强.国外钻井技术发展现状[J].断块油气田,2000, 7(5):64-68.Zhang D H, Xi J Q. Development status of foreign drilling technology[J]. Fault-Block Oil&Gas Field, 2000, 7(5):64-68.
[11] Ramamurthi K, Shaver D P, Agogino A M. Real time expert system for predictive diagnostics and control of drilling operation[C]//Artificial Intelligence Applications, 1990. Sixth Conference on. IEEE, 1990:62-69.
[12] Lai Z, Leng Y. Weak-signal detection based on the stochastic resonance of bistable Duffing oscillator and its application in incipient fault diagnosis[J]. Mechanical Systems and Signal Processing, 2016, 81:60-74.
[13] Kedadouche M, Thomas M, Tahan A. A comparative study between empirical wavelet transforms and empirical mode decomposition methods:application to bearing defect diagnosis[J]. Mechanical Systems and Signal Processing, 2016, 81:88-107.
[14]连晓圆.钻井过程中故障检测与诊断方法的研究[D].大连:大连理工大学, 2013.Lian X Y. Fault detection and diagnosis method in drilling process[D]. Dalian:Dalian University of Technology, 2013.
[15]孙正义,高兴坤.钻井卡钻事故预测及诊断专家系统模型的建立与实现[J].石油钻采工艺, 1996, 18(1):20-23.Sun Z Y, Gao X K. The establishment and implementation of drilling accident prediction and the model of diagnosis expert system[J]. Oil Drilling&Production Technology, 1996, 18(1):20-23.
[16]王宝毅,张宝生,费沿光,等.基于案例推理的钻井复杂情况专家系统[J].石油大学学报(自然科学版), 2005, 29(6):123-126.Wang B Y, Zhang B S, Fei Y G, et al. A drilling troubles expert system based on case-based reasoning[J]. Journal of China University of Petroleum(Edition of Natural Science), 2005, 29(6):123-126.
[17]郭建明,李琪,徐英卓.基于实例和规则集成推理的钻井事故诊断处理系统[J].石油钻探技术, 2007, 35(3):15-18.Guo J M, Li Q, Xu Y Z. Drilling accident diagnosis and treatment system based on case and rule integration reasoning[J]. Petroleum Drilling Techniques, 2007, 35(3):15-18.
[18]高晓荣,徐英卓,李琪.基于CBR的井下复杂情况与事故智能诊断和处理系统[J].计算机应用研究, 2008,(5):1446-1449.Gao X R, Xu Y Z, Li Q. Intelligent diagnosis and processing system of complex situation in well and accident based on CBR[J]. Computer Application and Research, 2008,(5):1446-1449.
[19]郭伟伟.基于故障树技术的远程故障诊断专家系统的研究[D].西安:西北工业大学, 2007.Guo W W. Research on remote fault diagnosis expert system basedonfaulttreetechnology[D].Xi.an:NorthwesternPolytechnical University, 2007.
[20]倪绍徐,张裕芳,易宏,等.基于故障树的智能故障诊断方法[J].上海交通大学学报, 2008, 42(8):1372-1375.Ni S X, Zhang Y F, Yi H, et al. Intelligent fault diagnosis method based on fault tree[J]. Journal of Shanghai Jiao Tong University,2008, 42(8):1372-1375.
[21]郭建明,王彦平.基于钻井工艺过程的事故诊断智能分析模型研究[J].西安石油大学学报(自然科学版), 2012, 27(3):50-53.Guo J M, Wang Y P. Research on accident diagnosis intelligent analysis model based on drilling process[J]. Journal of Xi.an Petroleum University, 2012, 27(3):50-53.
[22] Jolliffe I T. Principal component analysis and factor analysis[M]//Principal Component Analysis. New York:Springer, 1986:115-128.
[23]董顺,李益国,孙栓柱,等.基于状态空间主成分分析网络的故障检测方法[J].化工学报, 2018, 69(8):3528-3536.Dong S, Li Y G, Sun S Z, et al. Fault detection method based on state space-PCANet[J]. CIESC Journal, 2018, 69(8):3528-3536.
[24] Cui K, Gao Q, Zhang H, et al. Merging model-based twodimensional principal component analysis[J]. Neurocomputing,2015, 168:1198-1206.
[25]张媛媛,韩少廷,耿志强,等.自适应移动窗递推PCA过程监测方法研究[J].计算机与应用化学, 2011,(8):1022-1026.Zhang Y Y, Han S T, Geng Z Q, et al. Study on adaptive moving window recursive principal component analysis for process monitoring[J]. Computers and Applied Chemistry, 2011,(8):1022-1026.
[26]胡云鹏.基于主元分析的传感器故障检测盲区预测[J].化工学报, 2017, 68(4):1510-1515.Hu Y P. Blind zone prediction for PCA-based sensor fault detection[J].CIESC Journal, 2017, 68(4):1510-1515.
[27]彭必灿,张正道.基于稀疏主元分析的过程监控研究[J].计算机工程与应用, 2014,(18):240-245.Peng B C, Zhang Z D. Process monitoring research based on sparse principal component analysis[J]. Computer Engineering and Applications, 2014,(18):240-245.
[28]赵忠盖,刘飞.基于稀疏核主元分析的在线非线性过程监控[J].化工学报, 2008, 59(7):1773-1777.Zhao Z G, Liu F. Online nonlinear process monitoring based on sparse kernel principal component analysis[J]. Journal of Chemical Industry and Engineering(China), 2008, 59(7):1773-1777.
[29] Zass R, Shashua A. Nonnegative sparse PCA[C]//Proceedings of the Twentieth Annual Conference on Neural Information Processing Systems. Vancouver, British Columbia, Canada,2006.
[30]阮跃.基于案例,规则和模型推理的电站智能诊断系统[J].电站系统工程, 1998, 14(3):45-47.Ruan Y. Intelligent diagnosis system of power plant based on case, rule and inferential model[J]. Power System Engineering,1998, 14(3):45-47.
[31]张琦,孙劭文,李文鸿,等.基于案例推理的机械故障诊断方法探讨[J].解放军理工大学学报(自然科学版), 2004, 5(5):42-45.Zhang Q, Sun S W, Li W H, et al. Case-based reasoning for mechanical fault diagnosis[J]. Journal of PLA University of Science and Technology(Natural Science Edition), 2004, 5(5):42-45.
[32]李亚宁,王学雷,谭杰.基于PCR-多案例融合的焦化烟气脱硝过程指标优化设定[J].化工学报, 2018, 69(3):998-1007.Li Y N, Wang X L, Tan J. PCR-multi-case fusion method for setting optimal process indices of coking flue gas denitration[J].CIESC Journal, 2018, 69(3):998-1007.
[33]柳玉,贲可荣.案例推理的故障诊断技术研究综述[J].计算机科学与探索, 2011, 5(10):865-879.Liu Y, Ben K R. Survey of fault diagnosis based on case reasoning[J]. Journal of Frontiers of Computer Science and Technology,2011, 5(10):865-879.
[2] Gao F, Zhao X H, Liu H S, et al. The study of drilling safe monitoring system based on multi agent group[C]//Advanced Materials Research. Trans. Tech. Publications, 2012, 512:2565-2569.
[3]王林.钻井故障诊断专家系统的研究与实现[D].成都:电子科技大学, 2013.Wang L. The research and implementation of drilling fault diagnosis expert system[D]. Chengdu:University of Electronic Science and Technology of China, 2013.
[4]胡睿.钻井复杂情况与事故诊断专家系统研究[D].青岛:中国石油大学(华东), 2010.Hu R. Research on drilling complex incidents and accident diagnosis based on expert system[D].Qingdao:China University of Petroleum, 2010.
[5]张学洪,李黔.基于案例推理的井漏风险预警方法[J].断块油气田, 2017, 24(2):255-258, 263.Zhang X H, Li Q. Risk pre-warning method of well leakage based on case reasoning[J]. Fault-Block Oil&Gas Field, 2017, 24(2):255-258, 263.
[6]徐术国.基于录井数据的钻井事故预警专家系统[D].北京:中国石油大学(北京), 2011.Xu S G. Drilling accident warning expert system based on logging data[D]. Beijing:China University of Petroleum, 2011.
[7]马鹏鹏,周英操,蒋宏伟,等.钻井风险控制系统关键技术[J].石油钻探技术, 2014, 42(3):16-21.Ma P P, Zhou Y C, Jiang H W, et al. Key technology for drilling risk control system[J]. Petroleum Drilling Techniques, 2014, 42(3):16-21.
[8] Ferreiro S, Sierra B, Irigoien I, et al. Data mining for quality control:Burr detection in the drilling process[J]. Computers&Industrial Engineering, 2011, 60(4):801-810.
[9]袁俊和.钻井过程中的故障诊断方法研究[D].青岛:中国石油大学(华东), 2008.Yuan J H. Study of fault diagnosis methods in well drilling process[D]. Qingdao:China University of Petroleum, 2008.
[10]张东海,席继强.国外钻井技术发展现状[J].断块油气田,2000, 7(5):64-68.Zhang D H, Xi J Q. Development status of foreign drilling technology[J]. Fault-Block Oil&Gas Field, 2000, 7(5):64-68.
[11] Ramamurthi K, Shaver D P, Agogino A M. Real time expert system for predictive diagnostics and control of drilling operation[C]//Artificial Intelligence Applications, 1990. Sixth Conference on. IEEE, 1990:62-69.
[12] Lai Z, Leng Y. Weak-signal detection based on the stochastic resonance of bistable Duffing oscillator and its application in incipient fault diagnosis[J]. Mechanical Systems and Signal Processing, 2016, 81:60-74.
[13] Kedadouche M, Thomas M, Tahan A. A comparative study between empirical wavelet transforms and empirical mode decomposition methods:application to bearing defect diagnosis[J]. Mechanical Systems and Signal Processing, 2016, 81:88-107.
[14]连晓圆.钻井过程中故障检测与诊断方法的研究[D].大连:大连理工大学, 2013.Lian X Y. Fault detection and diagnosis method in drilling process[D]. Dalian:Dalian University of Technology, 2013.
[15]孙正义,高兴坤.钻井卡钻事故预测及诊断专家系统模型的建立与实现[J].石油钻采工艺, 1996, 18(1):20-23.Sun Z Y, Gao X K. The establishment and implementation of drilling accident prediction and the model of diagnosis expert system[J]. Oil Drilling&Production Technology, 1996, 18(1):20-23.
[16]王宝毅,张宝生,费沿光,等.基于案例推理的钻井复杂情况专家系统[J].石油大学学报(自然科学版), 2005, 29(6):123-126.Wang B Y, Zhang B S, Fei Y G, et al. A drilling troubles expert system based on case-based reasoning[J]. Journal of China University of Petroleum(Edition of Natural Science), 2005, 29(6):123-126.
[17]郭建明,李琪,徐英卓.基于实例和规则集成推理的钻井事故诊断处理系统[J].石油钻探技术, 2007, 35(3):15-18.Guo J M, Li Q, Xu Y Z. Drilling accident diagnosis and treatment system based on case and rule integration reasoning[J]. Petroleum Drilling Techniques, 2007, 35(3):15-18.
[18]高晓荣,徐英卓,李琪.基于CBR的井下复杂情况与事故智能诊断和处理系统[J].计算机应用研究, 2008,(5):1446-1449.Gao X R, Xu Y Z, Li Q. Intelligent diagnosis and processing system of complex situation in well and accident based on CBR[J]. Computer Application and Research, 2008,(5):1446-1449.
[19]郭伟伟.基于故障树技术的远程故障诊断专家系统的研究[D].西安:西北工业大学, 2007.Guo W W. Research on remote fault diagnosis expert system basedonfaulttreetechnology[D].Xi.an:NorthwesternPolytechnical University, 2007.
[20]倪绍徐,张裕芳,易宏,等.基于故障树的智能故障诊断方法[J].上海交通大学学报, 2008, 42(8):1372-1375.Ni S X, Zhang Y F, Yi H, et al. Intelligent fault diagnosis method based on fault tree[J]. Journal of Shanghai Jiao Tong University,2008, 42(8):1372-1375.
[21]郭建明,王彦平.基于钻井工艺过程的事故诊断智能分析模型研究[J].西安石油大学学报(自然科学版), 2012, 27(3):50-53.Guo J M, Wang Y P. Research on accident diagnosis intelligent analysis model based on drilling process[J]. Journal of Xi.an Petroleum University, 2012, 27(3):50-53.
[22] Jolliffe I T. Principal component analysis and factor analysis[M]//Principal Component Analysis. New York:Springer, 1986:115-128.
[23]董顺,李益国,孙栓柱,等.基于状态空间主成分分析网络的故障检测方法[J].化工学报, 2018, 69(8):3528-3536.Dong S, Li Y G, Sun S Z, et al. Fault detection method based on state space-PCANet[J]. CIESC Journal, 2018, 69(8):3528-3536.
[24] Cui K, Gao Q, Zhang H, et al. Merging model-based twodimensional principal component analysis[J]. Neurocomputing,2015, 168:1198-1206.
[25]张媛媛,韩少廷,耿志强,等.自适应移动窗递推PCA过程监测方法研究[J].计算机与应用化学, 2011,(8):1022-1026.Zhang Y Y, Han S T, Geng Z Q, et al. Study on adaptive moving window recursive principal component analysis for process monitoring[J]. Computers and Applied Chemistry, 2011,(8):1022-1026.
[26]胡云鹏.基于主元分析的传感器故障检测盲区预测[J].化工学报, 2017, 68(4):1510-1515.Hu Y P. Blind zone prediction for PCA-based sensor fault detection[J].CIESC Journal, 2017, 68(4):1510-1515.
[27]彭必灿,张正道.基于稀疏主元分析的过程监控研究[J].计算机工程与应用, 2014,(18):240-245.Peng B C, Zhang Z D. Process monitoring research based on sparse principal component analysis[J]. Computer Engineering and Applications, 2014,(18):240-245.
[28]赵忠盖,刘飞.基于稀疏核主元分析的在线非线性过程监控[J].化工学报, 2008, 59(7):1773-1777.Zhao Z G, Liu F. Online nonlinear process monitoring based on sparse kernel principal component analysis[J]. Journal of Chemical Industry and Engineering(China), 2008, 59(7):1773-1777.
[29] Zass R, Shashua A. Nonnegative sparse PCA[C]//Proceedings of the Twentieth Annual Conference on Neural Information Processing Systems. Vancouver, British Columbia, Canada,2006.
[30]阮跃.基于案例,规则和模型推理的电站智能诊断系统[J].电站系统工程, 1998, 14(3):45-47.Ruan Y. Intelligent diagnosis system of power plant based on case, rule and inferential model[J]. Power System Engineering,1998, 14(3):45-47.
[31]张琦,孙劭文,李文鸿,等.基于案例推理的机械故障诊断方法探讨[J].解放军理工大学学报(自然科学版), 2004, 5(5):42-45.Zhang Q, Sun S W, Li W H, et al. Case-based reasoning for mechanical fault diagnosis[J]. Journal of PLA University of Science and Technology(Natural Science Edition), 2004, 5(5):42-45.
[32]李亚宁,王学雷,谭杰.基于PCR-多案例融合的焦化烟气脱硝过程指标优化设定[J].化工学报, 2018, 69(3):998-1007.Li Y N, Wang X L, Tan J. PCR-multi-case fusion method for setting optimal process indices of coking flue gas denitration[J].CIESC Journal, 2018, 69(3):998-1007.
[33]柳玉,贲可荣.案例推理的故障诊断技术研究综述[J].计算机科学与探索, 2011, 5(10):865-879.Liu Y, Ben K R. Survey of fault diagnosis based on case reasoning[J]. Journal of Frontiers of Computer Science and Technology,2011, 5(10):865-879.
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