Abstract: Infrared spectral gas analysis technology has gradually become the main analytical method for gas logging owing to its advantages of non-pollution, high detection efficiency, and accurate analysis. However, because of factors, such as numerous types of hydrocarbon gases in the formation fluid and a large concentration range span, the measured spectral data are complicated. Therefore, the pre-processing of the spectral data is crucial as it directly impacts the accuracy of the measurement results. Noise is a significant interference factor, and improving the noise reduction process for the spectral data is crucial. To solve this problem, this study proposes a wavelet transform optimized ensemble empirical mode decomposition (EEMD) combined with Savitzky-Golay filtering (S-G) for the infrared spectral noise reduction algorithm. This algorithm first uses EEMD to decompose the signal to obtain a set of IMF components. It then uses wavelet transform for wavelet threshold denoising on the IMF components. Finally, the denoised IMF components are reconstructed, followed by S-G. The experimental results show that the algorithm can not only remove the Gaussian white noise and impulse noise in the absorption spectrum but also improve the smoothness index of the absorption spectrum and enhance the accuracy of logging gas detection.