Comparison and evaluation of several models in prediction of asphaltene deposition profile along an oil well: a case study

摘要

Deposition of asphaltenes on the inner surface of oil wells and pipelines causes flow blockage or significant production loss in these conduits. Generally, asphaltenes are stable in reservoir condition; however, change in pressure, temperature, and composition can trigger phase separation and then deposition of asphaltene along the flow stream. Therefore, it is required to identify the possibility of asphaltene precipitation and accurately quantify deposition tendency of these heavy organic molecules. This work is aimed at detailed assessment of the predictive capability of five deposition models available in the literature for calculating the magnitude and profile of asphaltene deposition in wellbores. To end this, firstly we discuss and describe these five models known as Friedlander and Johnstone (Ind Eng Chem 49:1151–1156, 1957), Beal (Nucl Sci Eng 40:1–11, 1970), Escobedo and Mansoori (SPE annual technical conference and exhibition, 1995), Cleaver and Yates (Chem Eng Sci 30:983–992, 1975), and Jamialahmadi et al. (Int J Heat Mass Transf 52:4624–4634, 2009). Afterward, thermodynamic modeling of live oil and a wellbore P–T relationship of the flowing fluid were used in a graphical method in order to identify asphaltene precipitation zone along axial wellbore length. Then, the five models were applied to the wellbore to forecast the deposition tendency of precipitated asphaltene particles and to obtain a profile of deposited asphaltenes. Most importantly, a measured deposit profile of the investigated wellbore enabled us to select the most accurate one for estimating the asphaltene deposition rate. The validation method presented in this work reveals that Cleaver and Yates (1975), Jamialahmadi et al. (2009), and Escobedo and Mansoori (1995) models have a satisfactory performance in predicting asphaltene deposition profile along the wellbore when compared to caliper measurement of the well.