- 【Updated on May 12, 2025】 Integration of CiNii Dissertations and CiNii Books into CiNii Research
- Trial version of CiNii Research Knowledge Graph Search feature is available on CiNii Labs
- 【Updated on June 30, 2025】Suspension and deletion of data provided by Nikkei BP
- Regarding the recording of “Research Data” and “Evidence Data”
Predicting Asphaltene Precipitation by Simple Algorithm Using Solubility Parameter Calculated Based on Peng-Robinson Equation of State
-
- Jamshidnezhad Mohammad
- Chemical Engineering Dept., Mahshahr IslamicAzad University
Bibliographic Information
- Other Title
-
- 溶解パラメーター推算にPeng-Robinson状態方程式を使用した簡易アルゴリズムによるアスファルテン析出挙動の予測
Search this article
Description
Asphaltene precipitation is frequently the cause of increased cost of oil production in the petroleum industry. To avoid or minimize problems due to asphaltene precipitation, a model to predict the amount of asphaltene precipitation under the petroleum reservoir conditions is required. In this study, the Flory-Huggins solution theory with a correctly tuned equation of state for calculation of the solubility parameter of liquid oil and a second order polynomial equation for variations of asphaltene solubility with pressure were applied to model asphaltene precipitation. The advantage of this model is that expensive and time consuming experiments are not required to obtain the asphaltene and liquid oil solubility parameters. Routine pressure/volume/temperature (PVT) tests and the amount of asphaltene precipitated at the bubble point pressure are sufficient. Data generated by the model were compared to the experimental asphaltene precipitation data on two live oils under reservoir conditions, showing that the model could accurately represent the behavior of asphaltene precipitation in the reservoir.
Journal
-
- Journal of the Japan Petroleum Institute
-
Journal of the Japan Petroleum Institute 51 (4), 217-224, 2008
The Japan Petroleum Institute
- Tweet
Keywords
Details 詳細情報について
-
- CRID
- 1390282680166283648
-
- NII Article ID
- 10022018250
-
- NII Book ID
- AA11590615
-
- COI
- 1:CAS:528:DC%2BD1cXotlygtLs%3D
-
- ISSN
- 1349273X
- 13468804
-
- NDL BIB ID
- 9564650
-
- Text Lang
- en
-
- Data Source
-
- JaLC
- NDL Search
- Crossref
- CiNii Articles
-
- Abstract License Flag
- Disallowed