Outlier detection in compositional grading data of a reservoir fluid is the main objective of the present study. The experimental data of a petroleum reservoir fluid including the mole fractions of the fluid components at different depths (from around 1000 to about 1400 m) and at constant temperature of 361.15 K are investigated. The utilized algorithm applies the basis of a mathematical approach, in which the statistical Hat matrix, Williams plot, and the residuals of a compositional grading model results bring about the probable outliers detection. The range of applicability of the applied model and quality of the existing experimental data are also investigated. The reported results of a previously developed model using the Soave–Redlich–Kwong equation of state (SRK EoS) with Peneloux volume correction are employed to evaluate the compositional analysis of the species in different depths of the fluid column. It is interpreted from the obtained results that the applied model for estimation of the compositional gradients has wide ranges of applicability. In addition, we may conclude that there is no outlier or probable doubtful datum in the investigated experimental datasets.