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Article Dans Une Revue Oil & Gas Science and Technology - Revue d'IFP Energies nouvelles Année : 2015

Predicting CO2 Minimum Miscibility Pressure (MMP) Using Alternating Conditional Expectation (ACE) Algorithm

O. Alomair
  • Fonction : Auteur
A. Malallah
  • Fonction : Auteur
A. Elsharkawy
  • Fonction : Auteur
M. Iqbal
  • Fonction : Auteur correspondant

Résumé

Miscible gas injection is one of the most important enhanced oil recovery (EOR) approaches for increasing oil recovery. Due to the massive cost associated with this approach a high degree of accuracy is required for predicting the outcome of the process. Such accuracy includes, the preliminary screening parameters for gas miscible displacement; the “Minimum Miscibility Pressure” (MMP) and the availability of the gas.All conventional and stat-of-art MMP measurement methods are either time consuming or decidedly cost demanding processes. Therefore, in order to address the immediate industry demands a nonparametric approach, Alternating Conditional Expectation (ACE), is used in this study to estimate MMP. This algorithm Breiman and Friedman [Brieman L., Friedman J.H. (1985) J. Am. Stat. Assoc. 80, 391, 580-619]estimates the transformations of a set of predictors (here C1, C2, C3, C4, C5, C6, C7+, CO2, H2S, N2, Mw5+, Mw7+ and T) and a response (here MMP) that produce the maximum linear effect between these transformed variables. One hundred thirteen MMP data points are considered both from the relevant published literature and the experimental work. Five MMP measurements for Kuwaiti Oil are included as part of the testing data. The proposed model is validated using detailed statistical analysis; a reasonably good value of correlation coefficient 0.956 is obtained as compare to the existing correlations. Similarly, standard deviation and average absolute error values are at the lowest as 139 psia (8.55 bar) and 4.68% respectively. Hence, it reveals that the results are more reliable than the existing correlations for pure CO2 injection to enhance oil recovery. In addition to its accuracy, the ACE approach is more powerful, quick and can handle a huge data.
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Dates et versions

hal-01931395 , version 1 (22-11-2018)

Identifiants

Citer

O. Alomair, A. Malallah, A. Elsharkawy, M. Iqbal. Predicting CO2 Minimum Miscibility Pressure (MMP) Using Alternating Conditional Expectation (ACE) Algorithm. Oil & Gas Science and Technology - Revue d'IFP Energies nouvelles, 2015, 70 (6), pp.967-982. ⟨10.2516/ogst/2012097⟩. ⟨hal-01931395⟩

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