Water hammer pressure diagnostics is a novel technique for fracture diagnosis developed in recent years. This advanced technique, with advantages of real-time monitoring and low cost, has potential for future applications. By analyzing the spectrogram of the pressure signal generated by pump shut-down in the fracturing process, this technique determines the response time corresponding to the pressure wave propagating from the fracture to the wellhead. Computing the product of the tube wave
velocity and reflection time, estimates the depth of the fracture. In the process of the fracturing pump shutdown, because of the fluid compressibility, the water hammer pressure wave signal can be obtained at the wellhead. The pressure wave signal contains a large amount of random noise and fixed frequency noise. This noise is generated by the pipeline vibration, hydraulic fractures opening and other bottom wave events. The existence of a lot of noise affects the accuracy of spectrum analysis, which leads to the challenge of determining the response time. The key problem of water hammer pressure wave diagnosis technology is to remove all kinds of random noise and fixed frequency noise. It is necessary to improve the signal-to-noise ratio (SNR) through reasonable filtering. In this paper, we analyzed the characteristics of the water hammer pressure wave signal and the effect of fil
tering. Firstly, noise is added to the purified water pressure wave signal, including random noise, fixed-frequency noise consisting of frequency close to the purified water pressure wave signal and frequency greatly different from the purified water pressure wave signal. The characteristics of the mixed signal after adding various noise is analyzed. After that, the superposition average filtering method, the finite impulse response (FIR) low-pass filtering method, adaptive filtering method and adaptive noise cancellation method are used to filter out the noise. The mechanism of these filtering methods and the effect of these filtering methods on SNR are studied. The SNR gain and the cepstrum effect are selected to evaluate the effectiveness of these filtering methods. In this paper, through theoretical analysis and simulation verification, the superposition average method can filter the
random noise. Besides, with an increase of stacking times, the degree of increase in SNR will gradually slow down. The adaptive noise cancellation method is the best method for fixed frequency noise filtering in this study. The adaptive noise cancellation method has the largest SNR gain and the best cepstrum recognition effect. This study can provide some technical guidance for signal filtering in a fracturing operation.
Key words：
cepstrum analysis; superposition average filtering method; FIR low-pass filtering method; adaptive filtering method; adaptive noise cancellation filtering method
HU Xiaodong, ZHOU Fujian, LI Yujiao, QIU Yang, LI Zhuolong. Filtering methods and characteristic analysis of water hammer pressure—wave signals from fracturing stop pumps. Petroleum Science Bulletin, 2021, 01: 79-91.
