Reservoir predication based on synchrosqueezing wavelet transform
Received date: 2016-10-20
Revised date: 2016-12-08
Online published: 2017-02-10
With the increasing difficulty of exploration,the accuracy of the time-frequency resolution of wavelet transform has been difficult to meet the requirements of the actual exploration targets,which needs to explore a higher resolution time-frequency analysis method.The synchrosqueezing wavelet transform (SWT) can obtain time-frequency resolution better than the wavelet transform by squeezing and reconstructing complex coefficient spectra in frequency direction.And it also has reversibility and anti-noise property.The simulation results show that the synchrosqueezing wavelet transform has better accuracy in characterizing the time-frequency characteristics of the signal.And in the analysis of the actual seismic data,the frequency division process of the synchrosqueezing wavelet transform is applied to process the thin gas-bearing reservoir seismic data.Where the low frequency appears obvious anomalies,and the lower frequency is,the more obvious anomalies are.So the reservoir is predicted effectively.
Li Bin，Yue You-xi，Wen Ming-ming . Reservoir predication based on synchrosqueezing wavelet transform[J]. Natural Gas Geoscience, 2017 , 28(2) : 341 -348 . DOI: 10.11764/j.issn.1672-1926.2016.12.015
[1]Partyka G,Gridley J.Interpretational applications of spectral decomposition in reservoir characterization[J].The Leading Edge,1999,18(3):353-360.
[2]Gao Jianhu,Yong Xueshan,Liu Hong.Study of reservoir prediction technology in frequency domain[J].Natural Gas Geoscience,2007,18(6):808-812.[高建虎,雍学善,刘洪.频率域储层预测技术研究[J].天然气地球科学,2007,18(6):808-812.]
[3][KG*5/6]Tian Renfei,Yang Chunfeng,Hu Yu,et al.Thin lithologic reservoir identification with spectral decomposition techniques[J].Natural Gas Geoscience,2015,26(2):360-366.[田仁飞,杨春峰,胡宇,等.识别岩性油藏薄储集层的谱分解技术[J].天然气地球科学,2015,26(2):360-366.]
[4]Lu Yi,Wu Xuesong,Shi Qianru,et.al.Applicability of seismic response to thin-interbed and acceptable condition of spectral decomposition[J].Natural Gas Geoscience,2010,21(4):660-665.[卢异,吴雪松,石倩茹,等.薄互层地震响应及分频技术的适用性分析[J].天然气地球科学,2010,21(4):660-665.]
[5]Daubechies I,Lu J,Wu H T.Synchrosqueezed wavelet transform:An empirical mode decomposition like tool[J].Applied and Computational Harmonic Analysis,2011,30(2):243-261.
[6]Meignen S,Oberlin T.A new algorithm for multicomponent signals analysis based on synchrosqueezing:with an application to signal sampling and denoising[J].IEEE Transactions on Signal Processing,2012,60(11):5787-5798.
[7]Herrera R-H,Han Jiajun,Mirko van dar Baan.Applications of the synchrosqueezing transform in seismic time-frequency analysis[J].Geophysics,2013,79(3):55-64.
[8]Shang Shuai,Han Liguo.Seismic data analysis using synchrosqueezing wavelet transform[C]//Expanded Abstracts of 83th SEG Annual International Meeting,2013:4330-4334.
[9]Shang Shuai,Han Liguo,Hu Wei,et al.Applied research of synchrosqueezing wavelet transform in seismic spectraldecomposition method[J].Geophysical Prospecting for Petroleum,2015,54(1):51-55.[尚帅,韩立国,胡玮,等.压缩小波变换地震谱分解方法应用研究[J].石油物探,2015,54(1):51-55.]
[10]Daubechies I,Maes S.A nonlinear squeezing of the continuous wavelet transform based on auditory nerve models[C]//Wavelets in Medicine and Biology.CRC Press,1996:527-546.
[11]Thakur G,Brevdo E,Fuckar N S,et al.The synchrosqueezing algorithm for time-varying spectral analysis:Robustness properties and new paleoclimate applications[J].Signal Processing,2013,93(5):1079-1094.
[12]Sinha S,Routh P S,Anno P D,et al.Spectral decomposition of seismic data with continuous wavelet transform[J].Geophysics,2005,70(6):19-25.
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