Abstarct: Since the seismic reservoir characterization have a very important role in all stages of exploration up to the development and production of hydrocarbon reservoirs, it is important to identify the relationship between reservoir properties and their elastic behavior, using rock physics model. The aim of this thesis is to improve rock physics models in hydrocarbon rocks using data fusion approach. The region being studied is an oil field located in southwest Iran, where the carbonates act as the producing reservoir. These fracture bearing carbonates make the study of this reservoir complicated. Moreover, the accuracy and the precision of microstructure and pore type can affect the rock physics models by either directing or distracting these models from the typical models. The geological studies (thin sections, Scanning Electron Microscope (SEM), Core CT-scan) show that the reservoir mainly contains large pores with aspect ratios between 0.1 and 0.9. In this study, the applications of five rock physics models are investigated: (1) Self Consistent Approximation following the Gassmann equation (SCA-G), (2) Xu-Payne which has been carried out using the Differential Effective Medium approach (X-P/DEM), (3) Kuster-Toksoz, (4) Xu-white and (5) Greenberg-Castagna. In addition, for improving model predictions, these rock physics models are fused by the three data fusion methods (1) Ordered Weighted Averaging (OWA) aggregation operator (2) Sugeno Fuzzy Integral and (3) Choquet Fuzzy Integral, that offer the most appropriate results. Furthermore, there are similarities among the mentioned models baxsed on the estimated values when compared to the measured Dipole Shear Sonic Imager (DSI) log. The results showed that the relative error in the estimation of shear wave velocity in wells A, B and C decreased from 21% to 4%, from 18% to 2%, and from 30% to 3%, respectively. Also, the relative error in the estimation of compressional wave velocity in wells decreased from 7.5 to 1 percent in well A, from 10.5 to 0.5 percent in well B, from 18 to 1.5 percent in well C. Ultimately, the analysis and results demonstrate that the OWA model gives the best compatibility with the original well log data. In other words, the reason why the OWA model provides the best results in the studied carbonates can be related to its optimization algorithm for defining model parameters.