基于小波变换的随钻测试数据降噪方法研究
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摘要
为消除随钻测试装置采集信号中的噪声,给工程人员提供准确的现场信息,提出了基于小波变换的方法对随钻测试数据进行降噪处理。首先,根据含噪声的随钻测试数据模型建立起小波变换降噪的基本流程。其次,在对含噪信号的小波变换特性分析的基础上,运用自相关函数对其在小波变换尺度空间中进行白噪声检验,确定小波最优分解层数。在分析对比小波降噪阈值的不同选取方式后,采用广义交叉确认理论来计算最优降噪阈值,从而在对信号降噪的同时还最大程度地保留了信号的有效特征成分。最后给出了基于该方法小波变换的随钻测试数据降噪的详细步骤,对实测的钻压和扭矩数据进行了小波降噪,取得了良好的降噪效果,为钻井工程后续分析提供了可靠的数据支撑,证明了本方法的优越性。
To provide accurate real-time information for engineer,a new method based on wavelet transform is proposed to eliminate the noise from signals which is sampled by the test device in oil drilling.First,a denoising flow based on wavelet is established according to the model of the oil-drilling testing data which contained much noise.Second,on the basis of analyzing the transforming characteristics of noisy signal based wavelet,a reasonable level of wavelet decomposition is calculated by using self-correlation function to estimate the white noise verification in wavelet scale spaces.Third,compared with different methods in wavelet threshold selection,an optimal denoising threshold is calculated using generalized cross validation(GCV) to hold the important features of signal and remove the noise from the signal.At last,the paper provides detailed steps of denoising the oil-drilling testing data based on the wavelet transform,which has obtained favorable results.The performance of pressure data and torque data confirms the usefulness of this approach,which provides a reliable data support to engineers for further analysis in drilling engineering.
To provide accurate real-time information for engineer,a new method based on wavelet transform is proposed to eliminate the noise from signals which is sampled by the test device in oil drilling.First,a denoising flow based on wavelet is established according to the model of the oil-drilling testing data which contained much noise.Second,on the basis of analyzing the transforming characteristics of noisy signal based wavelet,a reasonable level of wavelet decomposition is calculated by using self-correlation function to estimate the white noise verification in wavelet scale spaces.Third,compared with different methods in wavelet threshold selection,an optimal denoising threshold is calculated using generalized cross validation(GCV) to hold the important features of signal and remove the noise from the signal.At last,the paper provides detailed steps of denoising the oil-drilling testing data based on the wavelet transform,which has obtained favorable results.The performance of pressure data and torque data confirms the usefulness of this approach,which provides a reliable data support to engineers for further analysis in drilling engineering.
引文
[1]胡泽,陈平.应变测试法测试钻井参数的数据采集系统设计[J].西南石油大学学报,2007,29(3):49-52.
[2]胡泽,赖欣.基于DSP技术的钻井参数数据采集系统的设计[J].西南石油学院学报,2006,28(4):94-96.
[3]Parta P E.A successful application of fiber-optic-enabledcoiled tubing with distributed-temperature sensing alongwith pressures to diagnose production decline in an offshoreoil well[C].SPE 121696,2010.
[4]Dohono D L.Denoising by soft-thresholding[J].IEEETransaction on Information,1995,41(3):613-627..
[5]田养军,薛春纪.基于小波变换的遥感数据融合与边缘检测[J].地球科学与环境学报,2007,29(1):103-106.
[6]徐维秀,岳继光.基于多次小波变换的小波域阈值地震信号去噪[J].新疆石油地质,2007,28(6):764-767.
[7]杨济源,李晓平.小波变换在试井数据处理中的应用[J].钻采工艺,2009,32(5):42-44.
[8]王楠,杜劲松.小波消噪在振动信号处理中的应用[J].仪器仪表学报,2001,22(4):225-226.
[9]Jitendra Kikani.Multi-resolution analysis of long-termpressure transient data using wavelet methods[C].SPE48966,1998.
[10]杨建国.小波分析及其工程应用[M].北京:机械工业出版社,2005.
[11]Mallat S.Theory for multi-resolution signal decomposi-tion:the wavelet representation[J].IEEE Transactionson Pattern Analysis and Machine Intelligence,1989,11(7):674-693.
[12]郭科,倪秀静,许强.小波奇异性分析在滑坡体位移监测降噪中的应用[J].成都理工大学学报:自然科学版,2005,32(2):208-211.
[13]Ortiz C E P,Aguiar R B,Pires A P.Wavelet filtering ofpermanent downhole gauge data[C].SPE 123028,2009.
[14]李博,吴伟,周密.基于LabVIEW的小波阈值融合算法去噪分析[J].西安石油大学学报:自然科学版,2010,25(2):76-80.
[15]Mallat S,Hwang W L.Detection and processing withwavelets[J].IEEE Trans on Information Theory,1992,38(2):617-643.
[16]Box G E P,Jenkins G M,Reinsel G C.Time series analysis,forecasting and control[M].Englewood Clifs,NJ:PrenticeHall,1994.
[17]Jansen M,Malfait M,Bultheel A.Generalization crossvalidation for wavelet thresholding[J].Signal Process,1997,56(1):463-479.
[18]Ingrid Daubechies.Ten lectures on wavelets[M].CapitalCity Press,Montpelier,VT,1992.
[19]Shen X,Sikaneta S,Ramadhin J.Mud weight design fordeviated wells in shallow loose sand reservoirs with 3DFEM[C].SPE 130717,2010.
[20]何树山.大港油田大位移钻井技术研究与实践[J].西南石油大学学报:自然科学版,2008,30(2):104-108.
[2]胡泽,赖欣.基于DSP技术的钻井参数数据采集系统的设计[J].西南石油学院学报,2006,28(4):94-96.
[3]Parta P E.A successful application of fiber-optic-enabledcoiled tubing with distributed-temperature sensing alongwith pressures to diagnose production decline in an offshoreoil well[C].SPE 121696,2010.
[4]Dohono D L.Denoising by soft-thresholding[J].IEEETransaction on Information,1995,41(3):613-627..
[5]田养军,薛春纪.基于小波变换的遥感数据融合与边缘检测[J].地球科学与环境学报,2007,29(1):103-106.
[6]徐维秀,岳继光.基于多次小波变换的小波域阈值地震信号去噪[J].新疆石油地质,2007,28(6):764-767.
[7]杨济源,李晓平.小波变换在试井数据处理中的应用[J].钻采工艺,2009,32(5):42-44.
[8]王楠,杜劲松.小波消噪在振动信号处理中的应用[J].仪器仪表学报,2001,22(4):225-226.
[9]Jitendra Kikani.Multi-resolution analysis of long-termpressure transient data using wavelet methods[C].SPE48966,1998.
[10]杨建国.小波分析及其工程应用[M].北京:机械工业出版社,2005.
[11]Mallat S.Theory for multi-resolution signal decomposi-tion:the wavelet representation[J].IEEE Transactionson Pattern Analysis and Machine Intelligence,1989,11(7):674-693.
[12]郭科,倪秀静,许强.小波奇异性分析在滑坡体位移监测降噪中的应用[J].成都理工大学学报:自然科学版,2005,32(2):208-211.
[13]Ortiz C E P,Aguiar R B,Pires A P.Wavelet filtering ofpermanent downhole gauge data[C].SPE 123028,2009.
[14]李博,吴伟,周密.基于LabVIEW的小波阈值融合算法去噪分析[J].西安石油大学学报:自然科学版,2010,25(2):76-80.
[15]Mallat S,Hwang W L.Detection and processing withwavelets[J].IEEE Trans on Information Theory,1992,38(2):617-643.
[16]Box G E P,Jenkins G M,Reinsel G C.Time series analysis,forecasting and control[M].Englewood Clifs,NJ:PrenticeHall,1994.
[17]Jansen M,Malfait M,Bultheel A.Generalization crossvalidation for wavelet thresholding[J].Signal Process,1997,56(1):463-479.
[18]Ingrid Daubechies.Ten lectures on wavelets[M].CapitalCity Press,Montpelier,VT,1992.
[19]Shen X,Sikaneta S,Ramadhin J.Mud weight design fordeviated wells in shallow loose sand reservoirs with 3DFEM[C].SPE 130717,2010.
[20]何树山.大港油田大位移钻井技术研究与实践[J].西南石油大学学报:自然科学版,2008,30(2):104-108.
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