Quantitative prediction of crude oil properties (e.g. API gravity, kinematic viscosity, sulfur content) can be conducted using multivariate calibration models. The growth and the popularization of chemometrics allowed the analysis of larger data sets imply in models with better performance parameters. Nuclear magnetic resonance (NMR) and infrared (IR) spectroscopy are two analytical techniques that generate a high amount of compositional information. They have been recommended to predict crude oil properties, to reduce cost, time and volume of sample used to access oil quality. The objective of this paper is to provide a general overview of the application of 1H and 13C NMR and near (NIR) and mid (MIR) infrared spectroscopy techniques associate with chemometrics applied in characterization of crude oils. Among these four acquisition methods, the most used was 1H NMR. On the other hand, 13C NMR still is the least method applied, due to the low natural isotopic abundance of 13C that adds complexity and higher cost on this technique. The key point is that NMR and IR are well established to determine several physicochemical properties of crude oil. Regarding the regression methods, partial least square (PLS) is the most used. It combines simplicity and good performance for chemical data. Meanwhile, the application of nonlinear methods to predict properties that have not yet been well modeled can be explored.

Water-in-oil emulsions (W/O) are very stable in heavy oils due to the high amount of polar compounds. Natural products are rich sources of substances with polar characteristics, so the present work studied the effect of the addition of lignin and aroeira extract in W/O emulsions in heavy oil (13.7 degrees API) with 7 wt% of asphaltenes and 32 wt% of resins, respectively. W/O emulsions were prepared using: formation water; formation water with lignin in natura; formation water with modified lignin and formation water with aroeira extract (at concentrations 0.5 and 3.0% w/v). The emulsions were homogenized at 5000 rpm for 3 min. The homogeneity and stability of the emulsions were verified by photomicrography and droplet size distribution (DSD) using an optical microscope. Emulsions spectra were obtained in the mid-region infrared and these data were analysed by principal component analysis (PCA). The results showed that lignin in natura and modified lignin acted as emulsifying agents when added in low concentration (0.5% w/v). PCA showed that the emulsion prepared with 30% w/v aqueous phase containing tannin (aroeira extract) had a different response as compared with the other W/O emulsions. This suggests that W/O emulsions prepared with formation water and lignin (in natura and modified) did not alter the oil chemical profile.

Determining the physicochemical properties of petroleum is important for rapid decision-making during the production process. True boiling point (TBP) curve is the most important parameter in the petroleum characterization. However, the TBP can be estimated by high temperature gas chromatography (HTGC). In this paper, the HTGC technique associated with PLS regression was used to estimate API gravity, kinematic viscosity, pour point, carbon residue, saturated and aromatic content in crude oil. We use 98 samples with API gravity ranging from 11.4 to 54.0. Afterwards the developed methods were applied in nine samples from a field of production of the Brazilian coast. PLS model for API gravity, carbon residue, saturates and aromatics contents show root mean square error of prediction (RMSEP) set of 1.7, 0.83 wt%, 6.76 wt% and 4.05 wt% respectively. These models were applied to the nine samples and presented an exact equivalent to the models developed. The models for logarithm of kinematic viscosity and pour point show RMSEP of 0.31 and 12 degrees C respectively. Furthermore, tests applied in all models for evaluating the presence of systematic and trend errors indicate that there are no significant evidences of the presence of these types of error in the residues, at significance level of 5%.

Water-based drilling fluids are widely used in the petroleum exploration and production stage. After this stage, the residue contains several potentially polluting compounds, especially hydrocarbons. This waste is disposed of in industrial landfills for treatment, thus raising industrial operational costs. A combination developed of the residual oil from water-based drilling fluid waste. Relevant parameters were temperature, centrifugal force and the use of synthetic and natural polymers. Results showed that an increase in centrifugal force and temperature are essential for separation of the oil phase. The use of polymers improved efficiency by completely removing the oil from the residue, around 20% v/v. The characterization of the recovered oil classified it as light oil with density at 20 degrees C of 0.7997 g.cm(-3) and API gravity of 44.4, low sulfur content (0.0667 wt%), total acid number of 0.812 mg of KOH g(-1) , water content of 0.58% v/v and saturated, aromatic and polar contents of 64.11, 5.19 and 30.70 wt%, respectively. These results show that is possible to recover a viable oil that can be incorporated at the midstream stage, thus avoiding disposal and reducing operational costs and environmental impact.

The presence of hydrogen sulfide (H2S) poses many challenges in the production of petroleum at all stages of crude oil refinement and processing. Injection of an H2S scavenger into the hydrocarbon stream is most commonly used to reduce the concentration of H2S. Several H2S scavengers are commercially available, but each one must be examined to determine its suitability in terms of efficiency for capturing sulfide in petroleum. Therefore, in the present work, we proposed a system and process (hereinafter denoted as experimental setup/design) to test various H2S scavengers for their ability to efficiently capture sulfide in petroleum. To accomplish this, 46 samples of commercial H2S scavengers were tested using spectroscopy in the infrared region in association with Principal Component Analysis. It was concluded that samples with extreme pH and absorbance band intensity at 1175 cm(-1) greater than 0.095 showed the best efficiency for capturing sulfide in petroleum and that success of competing scavengers could be predicted based on these parameters.

Improper mixtures of: motor oil with crude oil; and derivatives mixed with other derivatives of lesser commercial value were identified in Brazil by companies in the energy sector. This study shows the great response that a portable NIR spectrometer had to discriminate crude oils and derivatives and to quantify them in blends (crude oils with used motor oil; and naphtha, gasoline, diesel, and kerosene). NIR spectra set were acquired in triplicate using a microNIR (TM) portable spectrometer, where it was possible to discriminate crude oil from used motor oil with 100% sensitivity, specificity, and precision. Regression models can quantify the oil content of a ternary mixture containing two crude oils (light and heavy oil) and a used motor oil with root mean square error of prediction (RMSEP) of 6.2 and 4.8 wt%, and R(2)p = 0.9871 and 0.9870 for support vector regression (SVR) and partial least squares (PLS), respectively. About the NIR spectra of naphtha, gasoline, diesel, and kerosene, partial least squares discriminant analysis (PLS-DA) allows the identification of any of these products with sensitivity, specificity, and precision of 100%. For the blends of gasoline and naphtha, the limit of detection (LOD), limit of quantification (LOQ), and RMSEP were 1.3, 4.4, and 1.4 wt%, respectively. Likewise, for diesel and kerosene blends, the PLS model allows the identification of the diesel with LOD, LOQ, and RMSEP of 2.8 wt %, 9.3 wt%, and 11.4 wt%, respectively.