No abstract

No abstract

No abstract

Wax deposition is a serious problem during oil production in the petroleum industry. Therefore, accurate prediction of this solid deposition problem can result in increasing the efficiency of oil/gas production. In this article, a novel approach is proposed to develop a predictive model for the estimation of wax deposition. An intelligent reliable model is proposed using a robust soft computing approach, namely, least-squares support vector machine (LSSVM) modeling optimized with the coupled simulated annealing (CSA) optimization approach. Our results demonstrate that there is good agreement between predictions based on the CSA-LSSVM model and experimental data on wax deposition. Furthermore, the performance of the newly developed model is compared with the performance of neural network and multisolid models for predicting wax deposition. The results of this comparison indicate that the proposed method is superior, in terms of both accuracy and generality, to the neural network and multisolid models. Finally, to check whether the newly developed CSA-LSSVM model is statistically correct and valid, the leverage approach, in which the statistical Hat matrix, the Williams plot, and the residuals of the model results lead to the identification of probable outliers, is applied. It is found that all of the wax deposition experimental data used in the present study seem to be reliable and that only one point is outside the applicability domain of the developed models for wax deposition.

No abstract

No abstract

The nature of aggregation kinetics of colloidal asphaltene particles in mixture of toluene and heptane was investigated by utilizing a polarizing microscope with appropriate magnification. The concentration of asphaltene in toluene _ heptane mixture varied from 1 g/l to 8 g/l. There are two main mechanisms associated with the aggregation process. These mechanisms are diffusion limited aggregation (DLA) and reaction limited aggregation (RLA). Each mechanism has its own characteristics and acts on limited values of asphaltene concentration. At the asphaltene concentrations below the critical micelle concentration (CMC), the DLA mechanism is dominant, while at concentrations above the CMC, at the initial stage of aggregation process, the RLA mechanism is observed and then the mechanism tends toward DLA (crossover behavior). It should be noted that the CMC for asphaltene in the solution is around 3 g/l.

Some of Iranian oil reservoirs suffer from operational problems due to asphaltene precipitation during natural depletion, so widely investigation on asphaltene precipitation is necessary for these reservoirs. In this study, a reservoir that is candidate for CO 2 gas injection process is selected to investigate asphaltene precipitation with and without CO 2 injection. In this case, asphaltene precipitation is monitored at various pressures and reservoir temperature. Then, a series of experiments are carried out to evaluate the amount of precipitated asphaltene by injection different molar concentrations (25%, 50%, and 75%) of CO 2 . The results show that during primary depletion the amount of precipitated asphaltene increases with pressure reduction until bubble point pressure. Below the bubble point the process is reversed (i.e., the amount of precipitated asphaltene at bubble point pressure is maximum). The behavior of asphaltene precipitation versus pressure for different concentrations of CO 2 is similar to primary depletion. Asphaltene precipitation increases with CO 2 concentration at each pressure step. In the modeling part, solid model and Peng-Robinson equation of state are employed which show a good match with experimental results.