Mud Pulse Signal Extraction and Noise Cancellation based on Adaptive LMS and NLMS Algorithms Performance

Abstract

During logging-while drilling data collection and transmission, strong noise and interferential signals contaminate mud pulse encoded signals. Varying and unpredictable statistical noise characteristics severely influence logging mud pulse signal measurement precision and reliability; making the raw signal extraction from the received signal becoming the key problem. Adaptive filter algorithms perform statistical estimation of the unknown signal to eliminate noise signals and overcome the problem of fixed coefficient digital filters. This paper researches the adaptive noise cancellation method to realize filter performances based on field mud pulse signal submerged into noise signal. MATLAB simulations are used to implement and simulate adaptive filter least mean square (LMS) and normalized least mean square (NLMS) algorithms. Well-site field mud pulse signal characteristics simulation results are used to compare and analyse the filter algorithm performance capability. Simulations show that the mud pulse input signal contains the direct current (DC) offset frequency and changes the sign level and channel characteristics. Based on MATLAB simulation results analysis, mud pulse useful signal is extracted, direct current offset and noise frequency components suppressed. Experimental results of the proposed method demonstrate the feasibility to apply the solution into mud pulse logging-while drilling systems. The research has presented a systematic step-by-step noise cancellation method to overcome the undisclosed implemented steps or signal spectral characteristics of different existing drilling signal models and simulation environments.