Profiling Asphaltene Inhibitor Performance Using Asphaltene Dispersion, Particle Growth, and Onset Point Methods with Packed-Bed and Capillary Deposition Tests

Abstract

Abstract Asphaltene inhibitors (AIs) are commonly used to mitigate asphaltene deposition issues. AIs are believed to change asphaltene growth behaviors, including dispersion, aging, electrostatic interaction, and/or more parameters, and thus collectively impacting deposition rate. In this work, AI performance was profiled by different methods to identify the responsible mechanisms. Particle growth profiles were first obtained based on traditional Turbiscan method (ASTM D7061) for larger particle dispersions. Subsequently, a modified Turbiscan method was used to assess the asphaltene growth kinetics. The indirect method was used to detect onset point. Packed-bed (5″ length; 0.334″ ID; 1.5 mm dia. bead) and capillary (100 ft length; 0.03″ ID) deposition tests were done at the same deposition flow rate (0.15 mL/min), time (20 h), and temperature (75 °C). Non-deposited residue was removed by cyclohexane at experiment flow rate. An extra fast flow rate (0.5 mL/sec) wash was applied on packed-bed setup to differentiate between loose and firm deposits. Untreated sample, along with 150 ppm AI-1, and 150 ppm AI-2 samples were tested with a variety of methods. Overall, both AI-1 and AI-2 show great dispersion and delayed asphaltene particle growth compared to the untreated. The changes of asphaltene growth kinetics were reflected on packed-bed and capillary deposition results as deposition masses for AI-1 and AI-2 treated crudes decreased significantly compared to the blank. Capillary tests yielded less deposition compared to the packed-bed test counterparts. Some asphaltene particles are pushed out of the capillary before deposition due to the higher shear rate but deposited in when compared to packed-bed experiments. By adopting the fast flow rate wash on packed-bed column, we found high correlation between firm asphaltene deposit weight to capillary deposit weight. By combining different test methods, AI effectiveness is determined at different kinetic stages. The performance profile can provide a systematic method to build structure-property relationships. We believe the testing protocol can facilitate new molecule development.