Abstarct: The estimation of elastic behavior parameters and rock strength (mechanical properties of rock) through their relationship with petrophysical and geological properties plays an important role in geomechanical studies of reservoir formations (for example, wellbore stability assessment and pore pressure prediction). In this study, the estimation of the aforementioned parameters at the well scale was conducted through the analysis of shear wave velocity, while at the field scale; it was carried out by evaluating bulk density, compressional wave velocity, and shear wave velocity for the Ilam reservoir interval in one of the fields in Dasht-e-Abadan. The data used in this study are well logs reflecting lithology and porosity (CGR, RHOB, NPHI, DT) from 26 wells, shear wave velocity logs from two wells, the depth of the Ilam reservoir Top obtained from seismic reflection interpretation, and laboratory data (static Young's modulus and uniaxial compressive strength) from one well. At the well scale, after processing and correcting the well logs, shear wave velocity estimation was performed using multivariate regression analysis, artificial neural networks, and supervised cluster analysis (Rock-Typing). At the field scale, spatial distribution modeling of density and the slowness of compressional and shear waves was carried out using geostatistical approaches (ordinary kriging estimation and sequential Gaussian simulation baxsed on variogram) in the form of a static model of the Ilam reservoir interval. The modeling results were validated against data from a blind well. Additionally, to enhance the accuracy of the modeling, zoning of the Ilam horizon was conducted baxsed on petrorraphy studies and interpretation of well logs, and variography was performed for each zone. Using laboratory data from one well, dynamic estimations were adjusted to static values baxsed on appropriate empirical relationships, and Geo-Mechanical Units were classified using an unsupervised cluster analysis method.