The Prediction of Viscosity, Formation Volume Factor, and Bubble Point Pressure of Heavy Oil Using Statistical Analysis, Artificial Neural Networks, and Three-dimensional Modeling: A Comparative Evaluation
| Autor: | B. Y. Jamaloei, C. C. Markwart, C. J. Zunti, F. Torabi |
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| Rok vydání: | 2014 |
| Předmět: |
Petroleum engineering
Artificial neural network Renewable Energy Sustainability and the Environment Chemistry Oil viscosity Volume factor Energy Engineering and Power Technology Dimensional modeling Mechanics Physics::Fluid Dynamics Viscosity Fuel Technology Nuclear Energy and Engineering Statistical analysis Bubble point Enhanced oil recovery |
| Zdroj: | Energy Sources, Part A: Recovery, Utilization, and Environmental Effects. 36:874-889 |
| ISSN: | 1556-7230 1556-7036 |
| DOI: | 10.1080/15567036.2010.547926 |
| Popis: | The physical properties of the reservoir fluids are required for various applications, such as reservoir performance optimization, field development, and modeling of the numerous enhanced oil recovery processes. Some correlations have been developed that can predict the properties of the reservoir fluids. However, most of these correlations do not accurately predict the physical properties of the heavy oil. This study uses three approaches to develop new correlations for the calculation of the dead oil viscosity: statistical analysis, artificial neural networks, and three-dimensional modeling. The results indicate that the developed correlations in this study for prediction of the heavy oil viscosity outperform the previously developed correlations. Moreover, the statistical analysis is performed to develop new correlations for prediction of the bubble point pressure and the formation volume factor. The results of these correlations are in good agreement with the experimental values. Finally, recommendati... |
| Databáze: | OpenAIRE |
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