Accelerating Saturate, Aromatic, Resin, Asphaltene (SARA) Analysis for High-Fidelity Petroleum Profiling via μSARA-HPLC
Ibrahim Atwah, Maram AlSaif, Muhammad Usman, Mohammed Abualreesh
Abstract
High Resolution Image Download MS PowerPoint Slide Saturates, aromatics, resins, and asphaltenes (SARA) analysis is essential for petroleum characterization, providing critical insights into crude oil properties that impact exploration, production, refining, and transport. However, many current methods have issues such as long runtime, system complexity, and reproducibility challenges. To address these issues, this study introduces μSARA-HPLC, an optimized, automated SARA analysis system using high-performance liquid chromatography (HPLC) with fraction collection. The system features three columns: a Polaris Si-SA MetaGuard column for asphaltene precipitation, a ZORBAX SB-CN column for resin separation, and a ZORBAX RX-SIL column for aromatics. The solvent gradients are carefully controlled, with mid-run flow reversal to ensure comprehensive separation. Elution windows are set using model compounds and verified via gas chromatography–mass spectrometry (GC–MS) for saturates and aromatics and Fourier transform ion cyclotron resonance mass spectrometry (FT–ICR–MS) for resins and asphaltenes. The system runs at 1.5 mL/min with n -pentane, dichloromethane, methanol, and isopropanol, with each run completed in 35 min, reducing the analysis time significantly compared with existing methods. Sample use is minimized and column life is extended by using injection volumes of 1.5–3 μL from 1:100 dilutions of oil (10 mg of oil in 100 μL of DCM). Dual detection using an evaporative light scattering detector (ELSD) and a diode array detector (DAD) provides exceptional sensitivity for all fractions, achieving detection limits as low as 0.05% by weight and an average recovery rate of 99.52%. Reproducibility was validated across crude oils and bitumen with varying API gravities, including extra-heavy and ultralight samples, using the NSO-1 reference oil over four months, which resulted in less than 1% deviation. This novel system provides a fast, reliable, and scalable approach to SARA analysis, advancing standardization in research and industry.