Research on Weak Signal Processing Methods for Electrical Imaging Logging Tools

Using the actual formation resistivity and porosity spectrum information, the electroimaging tool can quickly estimate the oil content of complex reservoirs, and can recognize the sedimentary structure and micro-cracks in tight oil and gas reservoirs by improving the measurement accuracy of the instrument. For electrical imaging logging, improving the detection accuracy of weak signals is a necessary condition for enhancing the quality of logging data, improving imaging resolution, and making imaging clearer. In response to the problem that current processing algorithms for electrical imaging logging signals cannot guarantee complete signal information while denoising, this paper establishes a logging noise signal analysis model based on the characteristics of logging signals, and uses wavelet transform and three adaptive LMS filtering algorithms to process the model. Simulation experiments on MATLAB show that the maximum mean square error of the adaptive LMS algorithm is greatly reduced than that of the wavelet method, which shows that the method has better filtering performance. Among the three adaptive LMS algorithms, the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1/2\text{LMS}$</tex> algorithm has the fastest running speed and the lowest MSE value, making it the most suitable for measuring weak signals underground.