Study on model-based pump noise suppression method of mud pulse signal

Mud pulse telemetry system plays a critical role in modern oil drilling engineering. However, the valid signal is complicated to be obtained because of the main disturbing of pump noises, especially in ultra-deep drilling operations. Therefore, four model-based noise suppression plans for the mud pulse signal pump are mainly proposed in this paper. First, three kinds of pump noise state-space models (linear time-invariant model, linear time-varying model, and nonlinear model) are constructed based on the detailed analysis of the pump noise characteristics. Then, the standard Kalman filter based on the linear system and the extended Kalman filter and unscented Kalman filter algorithm adopting the nonlinear system are respectively used to reconstruct and filter the pump noise. Moreover, the wavelet threshold denoising method based on a new power threshold function is specifically adopted to filter residual random noise after the removal of pump noise. Simultaneously, several noise-containing mud pulse signals with different SNRs (Signal-to-Noise Ratios) in the stable/unstable pump noise states were simulated. Besides, the noise suppression capability experiments of the four plans are deeply conducted. The results indicate that all the four plans can suppress and even remove the stable or unstable pump noise from the original signals: plan 1 and plan 2 have similar denoising effect, which can improve the SNR of signals with stable and unstable pump noise of different intensities by approximately 5-25 dB and 5-14 dB; while the plan 3 can increase the SNR of signals by about 4-20 dB and 2-8 dB; and plan 4 can increase that by about 4-20 dB and 1-8 dB, respectively. Under the comprehensive consideration of denoising ability, computation intensity and convergence speed, it is suggested that plan 1 integrating the time-invariant linear model and the standard Kalman filtering reveals superior performance. Furthermore, using the processing method consisting of plan 1 and wavelet threshold denoising method, the SNRs of the denoised signals can reach 6-7 dB even if the original SNR is as low as 30 dB. The proposed method is applied to the actual deep well mud pulse signal processing. The effective downhole pulse transmission waveform is successfully obtained, providing a highly reliable input signal for subsequent data restoration and contributing to an essential and practical significance for improving mud pulse transmission quality.