Well logging is an important tool for the characterization of subsurface rocks, being commonly used in the study of reservoir geology. It is well known that signals obtained as responses from geological media contain noise that can affect their interpretation, and that wavelet transform is more suitable than the Fourier transform to denoise non-stationary signals, as the ones obtained from well logs. On the other hand, there are several parameters that must be considered when working with wavelet transform, such as the choice of the wavelet basis function (mother wavelet), the decomposition level and also the function and rules that “control” which and how the coefficients will be used for signal reconstruction. This study analyzes the process of denoising well log data by discrete wavelet transform. Since the well log data are usually used in lithological classification, we propose a method associated with the k-nearest neighbor classification algorithm to investigate how different combinations of parameters affect the output signals and its performance in the classification, thus making it a data driven process. We propose a new thresholding function that shows better results when compared with traditional ones. The potential of wavelet transform as a tool to aid geological interpretation is evidenced by the identification of important geological features of the Namorado Field, Campos Basin, Brazil.