Unlike the Fourier theory that lacks scaling and translation parameters, the wavelet theory provides a new platform for the so-called de-oising workflow where noise in a signal or set of data is reduced. This work demonstrates how the wavelet transform can be used as a de-noising tool to denoise pressure transient data for PTA. The target is to create a smoother yet representative data pattern out of a noisy set of data. Two flow regimes were clearly identified from a noisy set of pressure transient data – spherical and radial flow regimes. The data points were then fitted to simple mathematical models describing spherical and radial flows. The p-values from the Chi-squared distributions were less than 0.05, indicating a perfect fit of the data points to the models.