Abstract
Compressional wave velocity (Vp) and shear wave velocity (Vs) are widely used as quick, easy to use, and cost-effective means of determining the mechanical properties of formations in the petroleum industry. However, shear wave logs are only available in a limited number of wells in an oil field due to the high cost of the log acquisition. For this reason, many attempts have been made to find a correlation between Vs and other petrophysical logs. In this study, a set of log data consisting of depth, neutron porosity (NPHI), density (RHOB), photoelectric (PEF), gamma ray (GR), caliper, true resistivity (RT), VP, and Vs were used to develop a model for Vs estimation in a well drilled in Ahvaz field. For this purpose, Tukey's method was employed to preprocess the data and eliminate outliers. Then, using the Non-dominated Sorting Genetic Algorithm II (NSGA-II), the best features were selected among the inputs to estimate Vs. Results indicate that increasing the number of input parameters of the model leads to an increase in accuracy and determination coefficient; however, this increase was negligible for the models with more than five input parameters. These parameters, i.e., Vp, RT, GR, RHOB, and NPHI, were selected as the best features to estimate Vs adopting Least Square Support Vector Machine (LSSVM) combined with Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Cuckoo Optimization Algorithm (COA). Results obtained from modeling reveal that LSSVM-COA algorithm produces a more accurate estimate of VS compared to LSSVM-PSO and LSSVM-COA in both training and testing steps. In addition, a very slight difference between the error in testing and training steps suggests a higher reliability of LSSVM-COA compared to two other hybrid algorithms. Comparing this model with empirical and regression ones clearly showed that the model error is considerably less than those models. Eventually, another set of data gathered in Ab-Teymour field was used to evaluate the model from the perspective of generalizability. Similarly, Vs estimation by LSSVM-COA model provided more reliable and accurate results compared to those of empirical and regression models, and also LSSVM-PSO and LSSVM-GA algorithms. Finally, it can be concluded that the method employed in this study could be used as an efficient means to accurately estimate Vs.
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