A modified cell-to-cell simulation model to predict oil-gas minimum miscibility pressure

被引:0
|
作者
Yang, Fulin [1 ,2 ]
机构
[1] Beibu Gulf Univ, Guangxi Key Lab Green Chem Mat & Safety Technol, Qinzhou 535011, Guangxi, Peoples R China
[2] Beibu Gulf Univ, Coll Petr & Chem Engn, Qinzhou 535011, Guangxi, Peoples R China
基金
中国国家自然科学基金;
关键词
Cell-to-cell simulation; Minimum miscibility pressure; Oil recovery factor; Phase equilibrium calculation; Evaluation method; ASPHALTENE-PRECIPITATION; CO2; STATE;
D O I
10.1007/s13202-024-01839-y
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
Calculating the minimum miscibility pressure (MMP) between crude oil and carbon dioxide (CO2) is critical for optimizing injection parameters, designing schemes, and predicting production capacity in CO2 injection projects for enhancing oil recovery. However, an accurate approach for obtaining this parameter is not yet established. In order to tackle this issue, a novel approach is suggested, based on the original cell-to-cell model, to determine the MMP and the 97% oil recovery rate as the standard. Using the volume-transformed Peng-Robinson equation of state enhances the precision of fluid volume estimation, as it mainly relies on predicting fluid volume within each cell. Furthermore, to ensure a precise estimation of the ultimate oil recovery rate, it is imperative to employ a total cell count of 500 in all simulations to avoid the problem of numerical dispersion. Finally, a second-order polynomial equation more accurately predicts the infinite-cell oil recovery factor. The accuracy of the modified model is verified by comparing MMP values from five oil and gas systems in the literature. The computational results of the modified multiple-mixing-cell (MMC) approach exhibit a higher level of concordance with the MMPs in the literature. The average relative error is less than 3.96%. The improved MMC algorithm can quickly determine the miscibility mechanism and visually represent the dynamic miscibility process involving multiple oil-gas contacts in a slim tube. This study provides a theoretical and practical basis for addressing the critical scientific issues of CO2-safe storage technology.
引用
收藏
页码:2529 / 2538
页数:10
相关论文
共 50 条
  • [31] The impacts of gas impurities on the minimum miscibility pressure of injected CO2-rich gas–crude oil systems and enhanced oil recovery potential
    Abouzar Choubineh
    Abbas Helalizadeh
    David A.Wood
    Petroleum Science, 2019, 16 (01) : 117 - 126
  • [32] A Pressure-Drop Model for Oil-Gas Two-Phase Flow in Horizontal Pipes
    Yang, Xinke
    Shi, Shanzhi
    Zhang, Hui
    Yang, Yuzhe
    Liu, Zilong
    Liao, Ruiquan
    Ribeiro, Joseph X. F.
    FDMP-FLUID DYNAMICS & MATERIALS PROCESSING, 2021, 17 (02): : 371 - 383
  • [33] Modified vanishing interfacial tension (VIT) test for CO2-oil minimum miscibility pressure (MMP) measurement
    Ghorbani, Mehdi
    Momeni, Ali
    Safavi, Saied
    Gandomkar, Asghar
    JOURNAL OF NATURAL GAS SCIENCE AND ENGINEERING, 2014, 20 : 92 - 98
  • [34] The impacts of gas impurities on the minimum miscibility pressure of injected CO2-rich gas-crude oil systems and enhanced oil recovery potential
    Choubineh, Abouzar
    Helalizadeh, Abbas
    Wood, David A.
    PETROLEUM SCIENCE, 2019, 16 (01) : 117 - 126
  • [35] A reliable strategy to calculate minimum miscibility pressure of CO2-oil system in miscible gas flooding processes
    Ahmadi, Mohammad Ali
    Zendehboudi, Sohrab
    James, Lesley A.
    FUEL, 2017, 208 : 117 - 126
  • [36] CO2-oil minimum miscibility pressure model for impure and pure CO2 streams
    Shokir, Eissa M. El-M.
    JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING, 2007, 58 (1-2) : 173 - 185
  • [37] A CO2-oil minimum miscibility pressure model based on multi-gene genetic programming
    Rezaei, Mehdi
    Eftekhari, Mahdi
    Schaffie, Mahin
    Ranjbar, Mohammad
    ENERGY EXPLORATION & EXPLOITATION, 2013, 31 (04) : 607 - 622
  • [38] A New Model for Predicting Minimum Miscibility Pressure (MMP) in Reservoir-Oil/Injection Gas Mixtures Using Adaptive Neuro Fuzzy Inference System
    Ayoub, M. A.
    Mohyaldinn, Mysara Eissa
    Manalo, Alexy
    Hassan, Anas M.
    Ahmed, Quosay A.
    ADVANCES IN MATERIAL SCIENCES AND ENGINEERING, 2020, : 527 - 545
  • [39] Estimation of minimum miscibility pressure of varied gas compositions and reservoir crude oil over a wide range of conditions using an artificial neural network model
    Choubineh A.
    Helalizadeh A.
    Wood D.A.
    Advances in Geo-Energy Research, 2019, 3 (01): : 52 - 66
  • [40] Effect of CO2 Concentration in Injecting Gas on Minimum Miscibility Pressure: Compositional Model and Experimental Study
    Shahrabadi, Abbas
    Dabir, Bahram
    Sadi, Maryam
    Fasih, Mehdi
    IRANIAN JOURNAL OF CHEMISTRY & CHEMICAL ENGINEERING-INTERNATIONAL ENGLISH EDITION, 2012, 31 (01): : 113 - 118