No abstract

Indigenous acidic crude oil compounds cause problems with regard to both the production and refining of crude oils. In this work, we have studied the molecular composition and interfacial properties of different acidic fractions. A North Sea acidic crude oil has been washed subsequently with pH 7, pH 10, and pH 14 aqueous solutions, resulting in three acidic fractions and three alkaline washed crude oils. The original crude oil, the acidic fractions, and the pH washed oils have been characterized by electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) and by Fourier transform infrared spectroscopy. The emulsion stability of water-in-oil emulsions of the original crude oil and the alkaline washed crude oils has been determined by the critical electric field cell method. Further, the interfacial properties of the acidic fractions and the alkaline washed oils have been examined. FT-ICR MS shows that 90% of the acidic compounds from this crude oil consist of carboxylic acids, with molecular weights in the range 300-800 Da. Removing the acidic compounds from the crude oils increases the interfacial tension and increases the water-in-oil emulsion stability, indicating that such indigenous acidic compounds destabilize water-in-oil emulsions.

Twenty heavy and/or particle-rich crude oils have been quantitatively fractionated into saturates, aromatics, resins, and asphaltenes (SARA) by asphaltene precipitation in n-hexane and highperformance liquid chromatography (HPLC). The newly developed and fully automated HPLC method has a sample capacity corresponding to 0.6 g of heavy crude oil. The crude oils have been characterized by vibrational spectroscopy in the near-infrared (NIR) and infrared (IR) regions. Principal component analyses (PCA) of the data sets from IR and NIR were performed so that exploratory data analyses could be conducted. Partial least-squares (PLS) regression models were built for each SARA component from IR and NIR data to predict the amounts of SARA components. These models successfully fitted the experimental data from NIR analyses and showed good predictive ability for the crude oil composition. The regression models from IR data were not modeled properly for aromatics and asphaltenes but were modeled excellently for saturate and resin components. For SARA determination, NIR spectroscopy appears to be a favorable alternative to the more time-consuming fractionation method.