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Published Nov 30, 2022

Chang Lu

Chuanwei Wang

Riyi Lin  

Abstract

The use of equation of state (EOS)-based phase behavior calculations is widespread in the petroleum industry, including the calculation of oil and gas reserves, production forecasting, and optimization of enhanced oil recovery (EOR) plans, surface separator design, and pipe flow calculation. The most commonly used method for providing phase behavior information is PT phase-equilibrium-calculation algorithms, which have been extensively studied for decades. However, simulation and engineering design of these processes using VT phase-equilibrium- calculation algorithms is sometimes more convenient than using conventional PT algorithms and has distinct advantages. The VT algorithm has been continuously improved over the last decade to ensure calculation accuracy, robustness, and efficiency, and it has been gradually applied in the petroleum industry. This article provides an overview of research findings in the field of EOS-based VT phase behavior calculation algorithms and their applications in oil and gas engineering. The Helmholtz-free-energy minimization approach, the Gibbs-free-energy minimization approach, and the nested approach based on the PT algorithm are three typical VT algorithm approaches discussed. The petroleum industry’s main applications of phase equilibrium calculation using the VT algorithm are described. Furthermore, some existing problems are identified, and several prospects for the application of the VT algorithm in the petroleum engineering field are presented. A critical review of the current state of the VT algorithm process, we believe, will fill the gap by shedding light on the process’s flaws and limitations, future development areas, and new research topics.

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Keywords

Phase Behavior Calculation, Phase Equilibrium Calculation, VT Algorithm, Petroleum Industry

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How to Cite
Lu, C., Wang, C., & Lin, R. (2022). The Development and Application of EOS-based VT Phase Behavior Calculation Algorithms in Petroleum Industry. Science Insights, 41(6), 697–712. https://doi.org/10.15354/si.22.or028
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Original Article