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

Amin Gholami,Shahin Kord,Siyamak Moradi,Omid Mohammadzadeh,Bahram Dabir

doi:10.1007/s13202-015-0189-3

Abstract

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.

Highlights

Crude oil is a complex mixture which makes it very difficult to achieve thorough characterization at the level of individual molecular type (Ahmadi and Shadizadeh 2012)
Power-Law Committee Machine (PLCM) model assigns weight factors to each individual sub-model of Support Vector Regression (SVR) and Alternating Conditional Expectation (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

Summary

Introduction

Crude oil is a complex mixture which makes it very difficult to achieve thorough characterization at the level of individual molecular type (Ahmadi and Shadizadeh 2012). SARA separation technique is an example of such a group type analysis in which the crude oil is characterized in terms of four distinct classes of saturates, aromatics, resins, and asphaltenes. This separation is implemented based on differences in polarity and solubility (Rasuli Nokandeh et al 2012; Mohammadi et al 2012). No practical and accurate model is proposed yet to clarify the phase behavior of asphaltene precipitation mainly due to its complex structure and properties (Shirani et al 2012a, b). The available models for delineation of asphaltenes precipitation are categorized into two distinct classes: models that involve the use of asphaltene properties and the ones that are based on scaling approaches

Methods

Results

Conclusion