Genetic Algorithm (GA)-Based Correlations Offer More Reliable Prediction of Minimum Miscibility Pressures (MMP) Between Reservoir Oil and CO2 or Flue Gas
Notice bibliographique
Résumé
Abstract Two new genetic algorithm (GA)-based correlations were proposed for more reliable prediction of minimum miscibility pressure (MMP) between reservoir oil and CO2 or flue gas. Both correlations are particularly useful when experimental data are lacking and also in developing an optimal laboratory program to estimate MMP. The key input parameters in a GA-based CO2-oil MMP correlation, in order of their impact, were: reservoir temperature, MW of C5+, and volatiles (C1 and N2) to intermediates (C2-C4, H2S and CO2) ratio. This correlation, which has been successfully validated with published experimental data and compared to common correlations in the literature, offered the best match with the lowest error (5.5%) and standard deviation (7.4%). For a GA-based flue gas-oil MMP correlation, the MMP was regarded as a function of the injected gas solvency into the oil which, in turn, is related to the injected gas critical properties. It has also been successfully validated against published experimental data and compared to several correlations in the literature. It yielded the best match with the lowest average error (4.6%) and standard deviation (6.2%). Moreover, unlike other correlations, it can be used more reliably for gases with high N2 (up to 20%) and non-CO2 components (up to 78%), e.g., H2S, N2, SOx, O2 and C1-C4. Introduction The MMP is a vital design parameter for CO2 miscible flooding projects. It impacts both operational and reservoir engineering aspects during the flood. Therefore, an operator must investigate the effects of various factors on the MMP, and consequently, their eventual impact on the project's operational strategy and surface facilities. The main factors affecting MMP are: reservoir temperature, oil composition and injected gas purity. The reservoir temperature has a big impact on MMP, as the MMP increases with the increase in the temperature and decreases with its decrease(1–3). On the other hand, the oil composition has a significant impact on MMP in that the MMP increases with the increase in oil molecular weight. A high oil volatiles fraction (e.g. C1) in the reservoir oil causes the MMP to rise, whereas a high intermediates (e.g. C2-C4) fraction reduces the MMP(2). Furthermore, the presence of non-CO2 (e.g. H2S, N2, SOx and O2) in the injected gas affects MMP, either raising or lowering it depending on the type of the component. From an operational perspective, the existence of these non-CO2 components in the injected gas should not be treated as a rigid impediment. In fact, the existence of certain components such as H2S and SOx could have a positive impact, as they contribute towards lowering the MMP. The presence of C1 and N2, on the other hand, could be detrimental as they cause the MMP to rise. As the separation of such components from the gas is difficult and costly, the current trend is to use the flue gas stream as it is, provided such impurities are below a certain optimum level. A gas stream containing such non-CO2 components are referred to as either low-purity CO2 or flue gas in the context of this paper.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
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