## 4 Discussion  
This study focused on the comparison between the empirical and data-driven correlations for \(V_{s}\) prediction in the Asmari formation, a limestone reservoir in the Kharg Island offshore oilfields. Five different empirical correlations were utilized to estimate the \(V_{s}\) values. Moreover, the LR technique was utilized to extract the linear correlations between the real \(V_{s}\) and other well logging parameters. In addition, two data-driven models using MLR and GEP were generated to estimate the \(V_{s}\) in the study area. Based on the conducted research, it was found that in the absence of the adequate number of geomechanical parameters, the \(V_{p}\) or \(PIGT\) (porosity log) can be utilized to predict the \(V_{s}\) values through the simple linear regression. This hypothesis can be supported by the fact that the \(V_{p}\) and porosity are better indicators for the velocity of shear wave in porous fluid-bearing rocks. Fundamentally, the rock porosity is a determining factor in the magnitude of rocks' shear strength, which is of paramount importance in ground movement, land subsidence, fluid motion, reservoir compaction, etc. [72]. The lack of presence of the \(V_{p}\) and porosity in the developed mathematical correlations can intensely reduce the precision of the estimated \(V_{s}\) values. This is why the Anselmetti and Eberli equation, which links the \(V_{s}\) only to the rock density, delivered inappropriate results in this research. Hence, it is suggested to give more attention to the \(V_{s}\) results when using this correlation for \(V_{s}\) prediction in carbonate rocks. This research highlights the potential of data-driven methods in accurately estimating \(V_{s}\), which is a critical parameter for geomechanical modeling in hydrocarbon reservoirs. However, as it was analyzed, the accuracy of the data-driven models relies on the number and type of the input well logging parameters. Thus, if an optimal set of appropriate geomechanical parameters is not selected through a profound analysis, the different AI algorithms may not necessarily deliver the accurate \(V_{s}\) values. Therefore, for the estimation tasks performed using the AI algorithms, a preliminary analysis must be carried out to  
Figure 12: Comparison between the different linear and nonlinear methods in \(V_{s}\) prediction. determine the number and the type of the rock parameters which will be imported into the data-driven model. In reservoir engineering, the shear wave velocity is an essential parameter to calculate the mechanical properties of underground rocks. In the current research, the length of the investigated geological profile was 150 m. The ground temperature along this profile was approximately equal to 130 \({}^{\circ}\)C. If the investigated profile was much longer, it would be expected that the \(V_{s}\) showed a better correlation with temperature variation. This is due to the fact that the temperature variation changes the rheological characteristic of the rocks containing pore fluids as well as the poroelastic properties of the rocks [73; 74]. Hence, for future works, investigations are suggested to be carried out to unveil the link between the ground temperature and \(V_{s}\) variation. Moreover, in this research, the rock density was approximately constant, equal to 2.4 g/cm\({}^{3}\) for the whole depth interval (from 4350 m to 4500 m). The relationship between \(V_{s}\) and density aids in characterizing the subsurface properties of reservoirs, helping in hydrocarbon exploration and reservoir management by providing insights into the rock's rigidity and composition, though local calibration may be necessary for accuracy when considering factors such as porosity, lithology, and diagenesis. Since the density of a reservoir rock is closely tied to several critical reservoir characteristics, for future research in the oilfield, a longer depth interval can be studied to evaluate the effect of change in the rock density on the \(V_{s}\) variation.