Improving the estimation accuracy of titration-based asphaltene precipitation through power-law committee machine (PLCM) model with alternating conditional expectation (ACE) and support vector regression (SVR) elements

Asphaltene precipitation/deposition have been longstanding issues in petroleum industry which lead to decline in oil production and economical efficiency. Owing to severe undesirable issues associated with this phenomenon, it is crucial to develop a reliable, accurate, and robust approach for quantitative estimation of asphaltene precipitation. In the first section of this paper, amount of asphaltene precipitation from stock tank oil through titration process was estimated using two predictive methods of Support Vector Regression (SVR) as well as Alternating Conditional Expectation (ACE). A novel predictive method, the so-called Power-Law Committee Machine (PLCM) with constituents of SVR and ACE, was then employed for estimation of the amount of asphaltene precipitation. PLCM model assigns weight factors to each individual sub-model of SVR and ACE to specify the contribution of each particular model in the overall prediction of asphaltene precipitation. Optimal values of these weight factors were extracted by means of Genetic Algorithm (GA) since it was already inserted as the combiner in the structure of the PLCM model. To validate this predictive tool, experimental data collected from open source literature were compared against the model predictions. It was observed that PLCM model can estimate the amount of asphaltene precipitation with very high accuracy and it had more satisfactory prediction performance compared to the other models of SVR and ACE.