(257t) Prediction of Asphaltenes Precipitation upon Blending of Petroleum Fluids

Crude oil supply chain involves huge network of interconnected production wells and pipelines. Crude oils from different sources are blended with different proportion or spiked with petroleum fluids to meet buyer's specification prior to marketing and distribution. Crude oils from different sources have diverse composition and petroleum crudes with lower API gravity are likely to have higher asphaltene content. Dilution of heavy crude oil with solvents or lighter oils is common in petroleum industry. Higher paraffin content promotes asphaltene precipitation and precipitated asphaltene represents a serious concern. Therefore, there has been a high interest in the development of comprehensive thermodynamic models that could project asphaltene precipitation conditions.

A number of thermodynamic models based on EOS and regular solution theory have been developed to predict asphaltene precipitation conditions. Pseudo component representations of asphaltenes and VLE data of petroleum samples are exploited to estimate EOS or solubility parameters. However existing models are burdened with excessive use of empirical equations and some are very much specific to particular petroleum samples. In this work state-of-the-art predictive activity coefficient models are utilized to correlate and predict the asphaltene precipitation phenomena upon blending of petroleum products of different compositions. Attention is given to the hierarchical structure of asphaltene precipitation, i.e., the transition of dissolved asphaltene molecules to nano-aggregates and then to clusters. As petroleum fluids are composed of countless number of components, the major classes of petroleum components, i.e. saturates, aromatics, resins, are represented with gamma distribution functions.