and available online here Cet article fait partie du dossier thématique ci-dessous publié dans la revue OGST, Vol. 70, n°6, pp. 909-1132 et téléchargeable ici D o s s i e rOil & Gas Science and Technology -Rev. IFP Energies nouvelles, Vol. 70 (2015), No. 6, pp. 909-1132 Copyright © 2015, IFP Energies nouvelles > Editorial -Enhanced Oil Recovery (EOR), Asphaltenes and HydratesÉditorial -EOR «récupération assistée du pétrole», Asphaltènes et Hydrates D. Langevin and F. Baudin ENHANCED OIL RECOVERY (EOR) > HP-HT Drilling Mud Based on Environmently-Friendly Fluorinated ChemicalsBoues de forage HP/HT à base de composés fluorés respectueux de l'environnement Assoc. 80, 391,, est utilise´e dans cette e´tude pour estimer les MMP. Cet algorithme recherche les transformations optimales d'un ensemble de facteurs pre´dictifs (ici C 1 , C 2 , C 3 , C 4 , C 5 , C 6 , C 7+ , CO 2 , H 2 S, N 2 , Mw 5+ , Mw 7+ et T) et d'une re´ponse (ici MMP) a`mode´liser qui produisent le maximum de corre´lations entre les facteurs et la re´ponse transforme´e. Cent treize points de donne´es MMP sont conside´re´s, issus al a fois de la litte´rature et de travaux expe´rimentaux. Cinq mesures MMP correspondant a`un champ pe´trolier koweı¨tien sont incluses dans les donne´es de test. Le mode`le propose´est valideé n utilisant une analyse statistique de´taille´e ; un coefficient de corre´lation de 0,956 est obtenu par comparaison avec les corre´lations existantes. De meˆme, l'e´cart type et la moyenne des valeurs d'erreurs absolues sont minimales : respectivement 139 psia (8,55 bar) et 4,68 %. Par conse´quent, il s'ave`re que les re´sultats sont plus fiables que les corre´lations existantes pour l'injection de CO 2 pur pour ame´liorer la re´cupe´ration du pe´trole. En plus de sa pre´cision, l'approche ACE est plus puissante, rapide et peut ge´rer un ensemble de donne´es e´normes.Abstract -Predicting CO 2 Minimum Miscibility Pressure (MMP) Using Alternating Conditional Expectation (ACE) Algorithm -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. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Miscible gas injection processes are among the effective methods for enhanced oil recovery (EOR). A key parameter in the design of gas injection project is the minimum miscibility pressure (MMP), whereas local displacement efficiency from gas injection is highly dependent on the MMP. Because experimental determination of MMP is very expensive and timeconsuming, searching for fast and robust mathematical determination of gas-oil MMP is usually wished. A new CO 2 MMP correlation based on Multiple-Linear-Regression modeling (MLR) technique has been successfully developed to more accurately estimate the CO 2 MMP for a wide range of live and heavy crude oils. The newly developed CO 2 MMP correlation is originated from CO 2 MMP experimental data in addition to database from the worldwide published literature that covers 103 pure CO 2 MMP data for various live and dead oil samples. The proposed model is trained by exploiting 80% (82 data points) of the data bank. This correlation is expressed as a function of reservoir temperature, C 7+ molecular weight, mole fractions of; non-hydrocarbon components (CO 2 , H 2 S and N 2 ), direct correlating components (C 1 , C 2 and C 5 ) and indirect correlating components (C 3 , C 4 , C 6 and C 7 ). Further, to investigate the authenticity in depth, the proposed correlation is compared with five most commonly used pure CO 2 MMP correlations from the literature. A statistical comparison is performed for both training data set (82 data points) as well as testing data set (21 data points). It is found that the proposed CO 2 MMP correlation provides the best reproduction of MMP data with a percentage average absolute error of 6.083% and 6.328% for training and testing categories respectively. Finally, the proposed model shows great performance using new set of samples compared with other correlations.

Miscible gas injection nowadays becomes an imperative enhanced oil recovery (EOR) approach 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-the-art MMP measurement methods are either time consuming or decidedly cost demanding. Therefore, in order to address the immediate industry demands a nonparametric approach (ACE) is employed in this study to estimate an important parameter MMP. ACE algorithm correlates optimal transforms of a set of predictors with an optimal response transform. Finally, the proposed model has produced a maximum linear effect between these transformed variables. More than 100 MMP data points are considered both from the relevant published literature and experimental work. The test data points also MMP measurements that are experimentally obtained for Kuwaiti crude Oil. The proposed model is validated using detailed statistical analysis and 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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