Modeling Asphaltene Precipitation-Part II: Comparative Study for Asphaltene Precipitation Curve Prediction Methods
Asphaltenes' solubility in crude oils is frequently affected by temperature, pressure, and oil composition changes. This could lead to the precipitation and deposition of asphaltene in various parts of the total production system, which would cause a significant economic impact. Predicting the...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
University of Baghdad
2025-01-01
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| Series: | Journal of Engineering |
| Subjects: | |
| Online Access: | https://www.joe.uobaghdad.edu.iq/index.php/main/article/view/3452 |
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| Summary: | Asphaltenes' solubility in crude oils is frequently affected by temperature, pressure, and oil composition changes. This could lead to the precipitation and deposition of asphaltene in various parts of the total production system, which would cause a significant economic impact. Predicting the conditions of asphaltene precipitation will be very useful in two cases. In the first case, without the problem, it will be useful in specifying the optimum operating conditions of oil production operations. In the second case, with the problem occurring, the prediction model will be useful in knowing the deposition areas and their sizes. This study is an extension of the first part, in which the advanced versions of Peng-Robinson (APR78 EOS) and Soave-Redlich-Kwong (ASRK EOS) cubic equations of state and cubic-plus-association equations of state (CPA EOS) were compared in predicting asphaltene precipitation conditions by using Multiflash software. The prediction was made for live crude oil (API gravity = 24˚ API) from an Iraqi oil field at different temperatures. The required data for modeling are fluid compositional analysis, PVT experiment data, and flow assurance data, which were collected from a fluid analysis report. It was noticed that the agreement in prediction was very high between the ASRK EOS and the CPA EOS for all temperature values and diverged from the APR78 EOS model at low temperatures (T = 50 and 40 ˚C). This study demonstrates the impact of selecting the appropriate model on predicting asphaltene precipitation and its influence on future predictions of the asphaltene deposition problem.
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| ISSN: | 1726-4073 2520-3339 |