Application Of The EMD Joint Time-frequency Analysis In Array Acoustic Signal

Acoustic logging is one of the main methods of geophysical logging. Full-wave column get by acoustic logging contains lots of information Developed a new, more scientific and effective methods of data processing and analysis of acoustic logging has become a major topic. Based on previous studies of various acoustic information extraction methods for acoustic logging signal and the nonlinear, non-stationary characteristic, this paper uses time-frequency analysis method to deal with the actual acoustic logging data obtained.The traditional time-frequency analysis methods include: short-time Fourier Transform, Wavelet Transform, Cohen class Time-frequency Distribution. But there are some disadvantages of various methods. The results of Short-Time Fourier Transform existence low resolution and poor adaptive disadvantage. The results of wavelet transform depended on the choice of wavelet basis, so this method also has the disadvantage of poor adaptive. Cohen class time-frequency distribution will resulted cross terms which is a false weight when analysis the multi-component signals, and that will influenced the correctness of the analysis in some extent.Hilbert-Huang transform is a new method based on instantaneous frequency. Using the empirical mode decomposition (EMD) method, the signal can be decomposed into a finite and often small number of intrinsic mode functions (IMF) which is a single-component signal. This method is suitable for analyzing nonlinear and non-stationary signals.The EMD method decomposes signals Based on there different characteristic scale of time into different IMF. Every IMF component corresponding to different time characteristics scales. Representative of the signal characteristics in different time scales of information. By doing smoothed pseudo Wigner-Ville distribution, these IMF components can be shown in time-frequency of the two dimensions. The various IMF components obtained by EMD were single-component signals which only have one frequency in one time and each time with different characteristic scale, therefore, also in a different frequency range. This method used in the analysis solved directly using Cohen class time-frequency distribution of multi-component signals produced cross-term issue to some extent. Effective to decompose the signal into different frequency bands of each IMF component to avoid cross-term arising from the frequency direction.Using this method to analysis many different acoustic full wave dates get from the Actual logging. Selected various waveform dates from the thick and Borehole Rules layer to analysis different nature of reservoir by the different characteristics from the time-frequency distribution and the affect of the existence of formation of cracks.(1) Using this method, the feature of different time-frequency distribution of the different time scales characteristics of the signal (different IMF components) can be found: The first IMF (IMF1) which has the highest frequency components of the waveform data performance the main features of the P-wave, the S-wave and follow-up pseudo-Rayleigh wave. The second IMF (IMF2) mainly displays the characteristics of high frequency Stoneley wave. The third IMF (IMF3) and the fourth IMF (IMF4), mainly displays the characteristics of the low frequency Stoneley wave.(2) Compared various time-frequency distributions of the dry layer, the regular pattern that the energy attenuation of some waves was influenced by the clay content was found: The higher clay content, the more serious variety of wave energy attenuation. Generally speaking, the effect of this impact was in a low degree, and the impact of the high frequency Stoneley wave was more serious than the other waves.(3) Compared various time-frequency distributions of the water layer, the regular pattern that the energy attenuation of Stoneley waves was influenced by the penetration rate was found: The higher penetration rate, the more serious Stoneley wave energy attenuation. Generally speaking, the effect of this impact was obviously, and the impact of the high frequency Stoneley wave was more serious.(4) Compared time-frequency distributions of the water layer with that of the oil layer, the regular pattern that the energy attenuation of S-wave and Stoneley were influenced by the oil saturation was found: The higher oil saturation, the more serious Stoneley wave energy attenuation, and the less serious S-wave energy attenuation. Generally speaking, the effect of this impact was obviously, and the impact of the S-wave and high frequency Stoneley waves was more serious than the other waves.(5) Because of the different reservoir parameters between the different layers, different layers have different time-frequency distributions, so we can identify each layer: Compared the dry layer with the water layer and the oil layer, the penetration rate was higher and the oil saturation was lower during the dry layer. So, reflected on the time-frequency distribution, the energy of the high frequency Stoneley wave that in IMF2 and the low frequency Stoneley wave that in IMF3 and IMF4 of the dry layer were higher usually, but the energy of the S-wave that in IMF1 of the dry layer was lower. Compared the water layer with the oil layer, the oil saturation of the oil layer was higher than that of the water layer. So, reflected on the time-frequency distribution, the energy of the S-wave was higher and the Stoneley wave was lower in the oil layer, especially the high frequency Stoneley.(6) In fractured formation, the center frequency of the P-wave and Stoneley wave will be shifted to lower frequency because of the strong absorption of formation.