Response characteristics of gas and water layers in tight sandstone reservoirs based on variational mode decomposition of array acoustic logging signals

• Autorzy: Liu Yuanqi , Lai Fuqiang , Huang Zhaohui , Chen Zhen , Kou Xiaopan , Jiang Guoqiang , Zang Yongqian

Identyfikatory

DOI

10.1007/s11600-022-00838-z

• Języki publikacji: EN

Abstrakty

EN

After the array acoustic full wave train is decomposed by the mode decomposition method based on time domain, there is serious mode aliasing between each Intrinsic Mode Function(IMF), which leads to the reservoir fluid response of time– frequency characteristics of each Intrinsic Mode Function that is not obvious. To solve this problem, this paper explores and compares the effects of Empirical Mode Decomposition, Ensemble Empirical Mode Decomposition, Complementary Ensemble Empirical Mode Decomposition, and Variational Mode Decomposition (VMD) in array acoustic full wave train decomposition, proves that using VMD method to decompose array acoustic full wave train has better effect, extracts the time–frequency distribution characteristics (including time edge, frequency edge and cumulative energy) of the first four full wave train Intrinsic Mode Functions, and analyzes response of time–frequency characteristics of Intrinsic Mode Functions on gas and water layers. The results show that the VMD decomposition can suppress the phenomenon of mode aliasing better, and each component represents different mode waves information in the full wave train. On features of time–frequency distribution, the peak arrival time and dominant frequency energy of IMF2, IMF3, and IMF4 show obvious response on the gas and water layer, and the cumulative energy of IMF2 and IMF3 components shows obvious response on the gas and water layer. Compared with the other three mode decomposition methods, the time–frequency characteristics of the Intrinsic Mode Function decomposed by VMD have an obvious response on the tight sandstone gas and water layer.

Słowa kluczowe

EN

cumulative energy; mode decomposition; array acoustic logging; time edge; frequency edge

PL

energia skumulowana

• Wydawca: Instytut Geofizyki PAN ; Springer
• Czasopismo: Acta Geophysica
• Rocznik: 2022
• Tom: Vol. 70, no 6
• Strony: 2675--2694
• Opis fizyczny: Bibliogr. 25 poz.

Twórcy

autor

Liu Yuanqi

• College of Petroleum and Natural Gas Engineering, Chongqing University of Science and Technology, Chongqing 401331, China

autor

Lai Fuqiang

• College of Petroleum and Natural Gas Engineering, Chongqing University of Science and Technology, Chongqing 401331, China

autor

Huang Zhaohui

• College of Petroleum and Natural Gas Engineering, Chongqing University of Science and Technology, Chongqing 401331, China

autor

Chen Zhen

• Exploration and Development Research Institute of Changqing Oilfeld Company, Xi’an 710021, China

autor

Kou Xiaopan

• Exploration and Development Research Institute of Changqing Oilfeld Company, Xi’an 710021, China

autor

Jiang Guoqiang

• College of Petroleum and Natural Gas Engineering, Chongqing University of Science and Technology, Chongqing 401331, China

autor

Zang Yongqian

• College of Petroleum and Natural Gas Engineering, Chongqing University of Science and Technology, Chongqing 401331, China

Bibliografia

• 1. Chen BT (2010) Application of EMD decomposition combined with time-frequency analysis in array acoustic signal (Master's thesis, Jilin University)
• 2. Dragomiretskiy K, Zosso D (2014) Variational mode decomposition. IEEE Trans Signal Process 62(3):531–544
• 3. Huang Ne (1996) Computer implicated empirical mode decomposition method Apparatus and article of manufacture. Appar Artic Manuf
• 4. Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q, Yen NC, Tung CC, Liu HH (1998) The empirical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysis. Proc R Soc Lond Ser A 45(4):903–995
• 5. Jin L, Li Y, Yang BJ (2005) Reduction of random noise for seismic data by time-frequency peak filtering. Prog Geophys 20(3):724–728
• 6. Lei Y, Chen H (2018) The velocity analysis of mixed array waveforms with different velocities. J Appl Acoust 37(02):298–306
• 7. Min X, Wang Z, Liu J (2015) Extracting array acoustic logging signal information by combining fractional fourier transform and choi-williams distribution. Appl Acoust 90:111–115
• 8. Ning QQ, Wang ZW, Yu Y, Xu FH, Wu HP (2020) Comparison of several time-frequency analysis methods for acoustic full waveform based on empirical mode decomposition. Pet Geophys Prospect 59(02):303–316
• 9. Ning QQ (2020) Research on time frequency characteristics of array acoustic logging signal (Master's thesis, Jilin University)
• 10. Torres ME, Colominas MA, Schlotthauer G, Flandrin P (2011) A complete ensemble empirical mode decomposition with adaptive noise. IEEE international conference on acoustics, speech and signal processing, pp:4144–4147.
• 11. Wang WW (2010) Signal analysis of array acoustic logging and its fluid identification method. Southwest Pet Univ
• 12. Wang ZW, Liu JH, Nie CY (2007a) Time-frequency analysis of array acoustic logging signal based on Choi-Williams energy distribution. Prog Geophys 22(5):1481–1486
• 13. Wang ZW, Liu JH, Nie CY (2007b) Rearrangement method of time-frequency analysis and its application in acoustic logging signal processing. J Jilin Univ: Earth Sci Edit 37(5):6
• 14. Wang ZW, Liu JH, Yue CW, Li XC, Li CC (2009) The filtering characteristics of HHT and its application in acoustic log waveform signal processing. Appl Geophys 6(001):8–16
• 15. Wang B, Tao G, Wang H, Tan BL (2011) Extracting near-borehole p and s reflections from array sonic logging data. J Geophys Eng 35(2):57–63
• 16. Wang ZW, Wang XL, Xiang M, Liu JH, Zhang XA (2012) Reservoir information extraction using a fractional Fourier transform and a smooth pseudo Wigner-Ville distribution. Appl Geophys 9(4):391–400
• 17. Wang F, Bian HY, Zhang Y, Duan C, Chen G (2016) Hilbert-huang transform combined with smoothed pseudo Wigner-Ville time-frequency distribution to identify reservoir fluid properties. Geophys Prospect Pet 55(06):851–860
• 18. Wu Z, Huang NE (2011) Ensemble empirical mode decomposition: a noise-assisted data analysis method. Adv Adapt Data Anal 1(1):1–41
• 19. Xiang M, Liu JH, Wang ZW (2015) Extracting array acoustic logging signal information by combining fractional fourier transform and choi-williams distribution. Applied acoustics
• 20. Xiang M, Parhat ZF (2018) Array acoustic logging’s characteristics in fractured formations based on the time-frequency domain analysis. Shiyou Diqiu Wuli Kantan/oil Geophys Prospect 53(4):849–857
• 21. Yl A, Bd A, Yl B, Hl B (2014) Local spatiotemporal time–frequency peak filtering method for seismic random noise reduction. J Appl Geophys 111(4):76–85
• 22. Yu HY, Xie CL, Wang Q (2014) Comparative analysis and applicability of array acoustic signal processing methods. West Explor Eng 26(06):67–72
• 23. Zhang CG, Wang GG, Liu YB, Jin ZW (1990) Study on the characteristics of pseudo Rayleigh wave and Stoneley wave and the first break of shear wave. Geophys Logg 14(6):385–391
• 24. Zhang CG, Jiang WZ, Pan HP (2009) Principle and application of acoustic logging. Petroleum Industry Press, Beijing
• 25. Zhong J (2011) Research on attribute information extraction method of array acoustic logging. Southwest Petroleum University

Uwagi

PL

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).