Enhancing the Logs Interpretation and Reservoir Parameter Characterization through Utilizing a Signal Processing Method

Logs data are valuable information that if well interpreted, can provide useful knowledge about the petrophysical properties of the reservoir but usually these data contain different types of noises thatmake their interpretation to some extent difficult and challenging. In this study, wavelet transform as a powerful signal processingmethod is taken into consideration for interpreting logs data to ١) reveal those aspects of data that cannot be identified easily bynormal investigation, and ٢) characterize the profile of static petrophysical parameters of the reservoir. Obtained results showthat gamma ray log has capability of detecting the flow units' alterations while sonic and resistivity logs well demonstrate the highporosity and permeability zones respectively. Method introduced inthis study is very useful in early reservoir characterization and formation evaluation where appropriate numbers of core data are not available or coring in a well is not economical