###### Abstract  
Accurate estimation of shear wave velocity (\(V_{s}\)) is crucial for modeling hydrocarbon reservoirs. The \(V_{s}\) values can be directly measured using the Dipole Shear Sonic Imager data; however, it is very expensive and requires specific technical considerations. To address this issue, researchers have developed different methods for \(V_{s}\) prediction in underground rocks and soils. In this study, the well logging data of a wellbore in the Iranian Aboozar limestone oilfield were used for \(V_{s}\) estimation. The \(V_{s}\) values were estimated using five available empirical correlations, linear regression technique, and two machine learning algorithms including multivariate linear regression and gene expression programming. Those values were compared with the real \(V_{s}\) data. Furthermore, three statistical indices including correlation coefficient (\(R^{2}\)), root mean square error (\(RMSE\)), and mean absolute error (\(MAE\)) were used to evaluate the effectiveness of the applied techniques. The mathematical correlation obtained by the GEP algorithm delivered the most accurate \(V_{s}\) values with \(R^{2}=0.972\), \(RMSE=0.000290\), and \(MAE=0.000208\). Compared to the available empirical correlations, the obtained correlation from the GEP approach uses multiple parameters to estimate the \(V_{s}\), thereby leading to more precise predictions. The new correlation can be used to estimate the \(V_{s}\) values in the Aboozar oilfield and other geologically similar reservoirs. artificial intelligence; gene expression programming (GEP); linear regression (LR); multivariate linear regression (MLR); shear wave velocity; well logging data; Kharg Island offshore oilfield; Pickett equation +
Footnote †: journal: Medical  
Moreover, the only wireline log tool to record the shear wave velocity is the Dipole Shear Sonic Imager (DSI) log, which is very expensive. For this purpose, various empirical correlations have been introduced for \(V_{s}\) estimation in different rocks [14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33]. For instance, some well-known correlations for \(V_{s}\) prediction in carbonate rocks can be found in [PERSON] et al. [18], [PERSON] [15], [PERSON] et al. [27], [PERSON] [14], and [PERSON] and Eberli [23]. Regardless of the rock type, those empirical correlations usually used one parameter to estimate the \(V_{s}\). It is clear that each extracted empirical correlation has its advantages and drawbacks. For example, [PERSON] used Poisson's ratio to predict the \(V_{s}\) values in reservoir rocks [32]. Nevertheless, the Poisson's ratio of rocks usually varies remarkably, and thus, the accuracy of the estimated values of \(V_{s}\) might be affected [15; 29]. In the past few years, the AI techniques have been widely used in geoscience applications such as reservoir characterization [34], geotechnics [35; 36; 37], mining exploration [38], earthquake engineering [39; 40], etc. Two of the frequent AI techniques are the MLR and GEP. MLR is a statistical method used in the field of machine learning and statistics to model the relationship between a dependent variable and two or more independent variables [41]. In fact, it is an extension of simple linear regression, which models the relationship between a dependent variable and a single independent variable. On the other side, the GEP approach has been broadly used in engineering projects, from hydraulics [42] to reservoir characterization [43]. GEP is a specific variant of genetic programming (GP) that emphasizes the representation and evolution of linear or tree structures using a process called gene expression. Generally, in GP, a population of candidate solutions (programs) is evolved over generations through the application of genetic operators such as mutation, crossover, and selection [44]. The MLR and GEP techniques were also applied to predict the \(V_{s}\) in rocks [45; 46; 47; 48; 49; 50; 51]. [PERSON] et al.