Crude characterization is critical for refinery optimization. Real-time knowledge of crude properties is the only way to ensure that full value is extracted from the crude, based on simulated plant operations and products quality. A good understanding of crude oil has an impact on planning and affects all refinery operations.
Crude quality is changing rapidly. In the past, oil wells were characterized by a crude assay that would change slowly over time, allowing fairly stable feedstock qualities. Now, the availability of shale oil and other opportunity crudes is a cause for wider feedstock variability.
Unknown fluctuations of feed properties introduce instability into the crude distillation unit (CDU), necessitating larger offsets from constraints. On the contrary, a closer monitoring of crude feed quality enables better optimization of the CDU by pushing against the appropriate constraints.
It is necessary to be able to characterize crude feeds more frequently and not to rely only on crude assays. The appropriate answer lies in a technology able to analyze, both rapidly and accurately, any crude feed received at the refinery. This technology should also assist in allocating the correct crude tanks, depending on quality, and allow the setting of CDU targets based on crude quality. Moreover, promptly knowing the crude quality will help planning and scheduling activities in setting target yields and qualities for process units.
Inline near-infrared (NIR) spectroscopy has been increasingly used to replace hazardous manual sampling and low-frequency laboratory analysis. (1) It is made possible by an appropriate modeling technology, allowing the prediction of the full property vector of any crude oil. Topology-based NIR models are able to identify and characterize any crude mixture. In less than 1 min, neat crudes ratios are predicted from any crude mixture, as well as properties such as full distillation curve (TBP), API gravity, total acid number, sulfur, SARA (saturate, aromatic, resin and asphaltene) and more. In addition, the full crude assays, integrating distillation cut yields and properties, can be delivered.
NIR spectroscopy. NIR range has been optimized for crude NIR analysis, as shown in FIG. 1. The best spectral domain to analyze crude oil was identified in the combination band, lying between 4,000 and 5,000 cm (-1), which provides the maximum of information without residual band absorption coming from the visible spectral region. Those residual absorptions would make the analysis, and therefore the model prediction, more unstable and inaccurate.
The spectrum of any product is directly linked to the physical and chemical properties of this product. It is like a fingerprint of the product. Topology modeling is based on spectra matching, working through pattern recognition and database densification. It means any spectrum is used as a fingerprint of the sample. This spectrum will be positioned in the database and characterized with the closest neighbor's spectra. Then, the full set of properties of the new sample will be predicted from the standard average of properties of closest spectra (FIG. 2). Indeed, the closest spectra means samples having the closest physical and chemical properties.
It does not...