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New efficient tool diagnoses asphaltene stability: Utilization of refractive index
Author(s) -
Ahmadi Mohammad Ali,
Zendehboudi Sohrab
Publication year - 2019
Publication title -
the canadian journal of chemical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.404
H-Index - 67
eISSN - 1939-019X
pISSN - 0008-4034
DOI - 10.1002/cjce.23448
Subject(s) - asphaltene , precipitation , process engineering , deposition (geology) , petroleum engineering , simplicity , stability (learning theory) , petroleum product , oil production , undercut , petroleum , computer science , environmental science , materials science , chemical engineering , geology , chemistry , meteorology , machine learning , engineering , composite material , organic chemistry , paleontology , philosophy , physics , epistemology , sediment
Asphaltene precipitation/deposition is considered a critical issue in the petroleum industry as it causes damages and blockage in various process equipment (e.g., pipe, pump, and vessel) and porous formation. To prevent this obstruction, it seems necessary to attain a reasonable estimate of the precipitation time, in other words, the time of asphaltene stability in crude oils. The gene expression programming (GEP) technique is utilized in this study to develop a correlation for the prediction of this important parameter using real refractive index (RI) data. The independent variables considered for the development of the correlation are the composition of aromatics, saturates, and resins in weight percentage. The predicted outputs show a better match with the real data, compared to the results obtained from other available predictive tools and/or correlations. Besides its simplicity, the developed correlation can have a broad range of applications in oil production plants in order to ensure that proper strategies are determined to avoid asphaltene precipitation/deposition throughout various stages of oil production and processing.