Estimating wave damping in carbonate rocks is complex due to their heterogeneous structure. For this reason, further research in this area is still necessary. Since the identification and evaluation of reservoir quality play an essential role in the optimal use of hydrocarbon resources, efforts are made to provide new solutions to achieve this goal by managing knowledge and accessing information from new tools such as the Vertical Seismic Profile (VSP). Seismic waves are deformed in frequency content and amplitude as they pass through the earth's layers. Part of the reduction in wavelength is related to the nature of the wave propagation and part to the geological properties, including porosity and fracture. Anisotropy and velocity model analysis, rather than the direct connection between reservoir parameters and seismic absorption coefficient, have received the majority of attention in earlier studies on the impact of reservoir parameters and fractures on changes in the quality factor. In this study, the correlation of the quality factor with parameters such as velocity deviation, fracture density, and permeability has been investigated, and an attempt has been made to define the quality factor as a tool to assess the quality of the reservoir. The statistical study using the multiple linear regression method found that fracture density is the most important parameter that follows the trend of the quality factor value. In the analysis, the quality factor showed a relatively good correlation with the permeability of the core data, so in the periods with maximum permeability, the quality factor had the lowest values. According to K-Means Clustering Analysis, 18% of the studied reservoir interval was evaluated as good quality, 33% as medium, 36% as poor, and 12% as hydrocarbon-free. This work provides insight into accessing reservoir quality using quality factor and velocity deviation logs and would be valuable for the development of reservoir quality prediction methods. Based on the study's results, it is recommended to apply this technique for modeling reservoir heterogeneity and assessing 2D and 3D seismic data to predict the reservoir quality of gas fields prior to drilling operations and reduce exploration risks.
