Systematic investigation of asphaltene precipitation by experimental and reliable deterministic tools

Asphaltene precipitation in oil operations contributes to serious technical and non-technical issues, which is affected by reservoir conditions such as pressure, temperature, dilution ratio, and type of diluent. In this work, a mathematical correlation is introduced to forecast the weight percent (wt%, g/g) of precipitated asphaltene in light oils. Experimental data are obtained based on high-resolution images taken from high-pressure cell processed by the image analysis method. Employing experimental data, a simple and precise correlation on the basis of the scaling/fractal theory is developed. In this study, the amount of asphaltene precipitation is considered in terms of pressure and dilution ratio in which the coefficients are simply calculated through the Vandermone matrix. Comparison between the model results and real data reveals a good match where the average coefficient of correlation (R-2) is above 0.95. Additionally, two previous predictive methods, namely the Bayesian belief network and scaling models, are utilized and the outputs are compared (e.g. the magnitudes of mean squared error (MSE) are 0.0065 and 0.038 for C6 and C7, respectively), implying the effectiveness of the new deterministic tool in terms of accuracy and reliability such that the average MSE is 0.006 for both C-6 and C-7. The trustworthiness of the real asphaltene precipitation data is also assessed by the statistical leverage approach based on the hat matrix, standardized residuals, and William plot to specify the applicability domain (AD) of the predictive models. It was found that all deterministic techniques presented in this study result in satisfactory accuracy, since the entire collected experimental data are reliable within AD.