Study on the Domain of Parameters in the Stochastic Reconstruction Method

Abstract

In the stochastic reconstruction of petroleum fractions, the domain configuration for parameters is of great significance to the performance of the model in terms of accuracy and convergence. Based on the stochastic reconstruction model of gas oils, this work investigates the influence of different domains on the simulating accuracy, the convergence rate and the ability to predict detailed composition. In this paper, the parameters to be optimized in the model include histogram distributions and gamma distributions, which are used to represent the structural attributes. In each histogram distribution, the parameters are real numbers between zero and one that should increase progressively. The mean and shape parameters are used as the tuning parameters for gamma distribution. As for the mean, the lower and upper boundaries are configured as fixed values that depend on the predefined values of the structural attributes. For the shape parameter, multiple cases are considered for its upper boundary, which is set to 5, 10, 20, 50 and 100, respectively. The lower boundary is set to 0 or 1, depending on the structural attribute. The results indicate that the simulating accuracy of the stochastic reconstruction model improves with the increase of the upper boundary for the shape parameter. When the upper boundary of each shape parameter is 20, the simulating accuracy is better than that when the upper boundary is 5 and 10, and close to that when the upper boundary is 50 and 100. The detailed group-type analysis is involved in the model to predict the distributions of mass fraction in chemical families against the carbon number. The results show that the predicted distributions in normal paraffins, isoparaffins, naphthenes, and aromatics are closest to the experimental distributions when the upper boundary is 20 and 50. When the upper boundary is 5 and 10, the predicted distributions in normal paraffins and isoparaffins are lower and broader than the experimental distributions. On the contrary, the predicted distributions in normal paraffins and isoparaffins are higher when the upper boundary is 100. By studying the effects of different domains on the results of the stochastic reconstruction model, a better molecular characterization of petroleum fractions can be achieved.