Abstarct: In hydrocarbon reservoir studies, gaining information about the petrophysical characteristics is the first priority. One of the significant petrophysical properties of any reservoir is porosity. Having knowledge about the distribution of porous horizons can be helpful in both identification of proper areas for drilling program and decreasing the probable danger of facing with any dry wells. One of the conventional methods of determining the distribution of these petrophysical properties (e.g. porosity) is to use of drilling various wells. Regarding the limitation of drilling data to specific points around the borehole and associated cost of drilling, utilizing of alternative methods is completely necessary. Quantitate descxription of different properties and structures of reservoir needs to diverse data including well logs, cores and seismic data as well as geological information. The most important factor in reservoir descxription is to combine all of the data available and making a high contrast and qualitative model. Well logs are capable of discrimination of features at depth while their lateral resolution is quite weak. In the other hand, seismic data have regular and wide distribution in reservoir region but they are not capable of providing any good contrast for depth. Hence, integration of these data (i.e. well logs and seismic data) can be a significant help for exact descxription of reservoirs studied. The prime aim of this study is to investigate the well logs and seismic data available in Abozar and Noroz oil fields. Afterward, utilizing the seismic and well logs data inversing methods including neural network and extracting novel seismic attributes, attempts have been carried out to describe the distribution of petrophysical parameters of reservoirs to obtain thickness variation and structural interpretation for suggesting the best procedure of exploration operation. In this regard, after verifying the obtained reservoirs parameters in the other areas, initial maps can be obtained via multi-linear regression analysis and neural network methods. For estimation of porosity distribution in the reservoir, first of all, sonic log data is corrected via available seismic data (i.e. check shot); geological horizons are determined, picked and interpreted in the seismic lines; synthetic wavelet and seismogram are extracted; well logs and seismic data have been calibrated; an initial model has been created and inverted for error correction; different methods including multivariate regression analysis and neural networks are used for determination of porosity and converting seismic and well logs data to porosity in the studied horizon; thickness specification and identification of the Burgan reservoir trend is done ultimately. For reaching to those aims mentioned earlier, in the first step, for making a synthetic seismogram, 2D seismic and commonly used well log data (e.g. RHOB, DT, PHI, etc) of the Noroz and Abozar oil field have been used. Regarding the capabilities of Hampson-Russell software in seismic inversion tasks, the most generalized acoustic impedance was extracted. Afterward, utilizing the multivariate regression analysis and cross correlation methods, three seismic attributes including acoustic impedance, average frequency and time have been extracted from seismic data. Ultimately, three different neural networks were taken into account for forecasting of porosity using extracted attributes. The results obtained have shown that probabilistic neural network is the one with the best prediction for porosity. The structural map obtained from geological studies has shown a Northwest-Southeast trend in the baxsement. Relative depth of baxsement is increased to southeast of the district. Slop of the west sections is comparatively more than that of the east and south sections. Iso-thickness maps obtained from drilling programs have shown that the thickness of sandstone section of Burgan in Kazhdomi formation is increased from east to west directed in the perpendicular direction of baxsement. As a result, Burgan reservoir in the region of wells located in Sfandiar filed in south of reservoir, regarding the porosity obtained from logs and core data, has a lower thickness and porosity compared to those of the wells located in the north and south regions. Briefly speaking, from Hendijan filed in the north of the reservoir toward the Sfandiar filed in the south, thickness and quality of reservoirs have been decreased significantly.