In this paper, we analyse the dependence of the energy sector stocks using the vine copula models and understand the information transfer between them using the entropy method. An information spillover network is constructed using total transfer entropy, allowing for identifying the path of information flow. We have used the national stock exchange’s most prominent energy sector stocks. The top ten energy stocks were chosen based on the most valuable fundamental factors like market capitalization and net profit. We analyse the daily close price of ten energy stocks from 2015 to 2023 for eight-year periods. To count the serial dependence in data, we have used the GJR-GARCH(1,1) model with standardized student-t distribution for each of the ten energy stocks for marginal density. The vine copula approach is employed to model the dependence among these stocks. The R-Vine model with a joint maximum likelihood approach with all copula families outperforms for dependence. These findings provide insight into the tail dependence relationships among stocks in the energy sector.