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Enregistrement W4210793783 · doi:10.2118/209208-pa

Static Adsorption Evaluation for Anionic-Nonionic Surfactant Mixture on Sandstone in the Presence of Crude Oil at High Reservoir Temperature Condition

2022· article· en· W4210793783 sur OpenAlexaff
Ahmed Fatih Belhaj, Khaled Abdalla Elraies, Juhairi Aris Shuhili, Syed Mohammad Mahmood, Raj Deo Tewari, Mohamad Sahban Alnarabiji

Notice bibliographique

RevueSPE Reservoir Evaluation & Engineering · 2022
Typearticle
Langueen
DomaineEngineering
ThématiqueEnhanced Oil Recovery Techniques
Établissements canadiensUniversity of Calgary
Organismes subventionnairesnon disponible
Mots-clésPulmonary surfactantAdsorptionChemistryEnhanced oil recoveryChromatographyAqueous solutionAlkylCrude oilChemical engineeringOrganic chemistryPetroleum engineeringGeology

Résumé

récupéré en direct d'OpenAlex

Summary The application of surfactants in enhanced oil recovery (EOR) has revealed over the years various challenges that impose limitations on the successful implementation of surfactant flooding. Surfactant adsorption is one of the most important aspects that strongly dictates the feasibility of surfactant-based EOR. The effect of the presence of crude oil on surfactant adsorption and the influence of surfactant partitioning on the adsorption quantification are presented in this paper. Static adsorption experiments were conducted in this study for a surfactant mixture [alkyl ether carboxylate (AEC):alkylpolyglucoside (APG)] on sandstone rock samples in the absence and presence of crude oil. Partitioning experiments were carried out to evaluate the surfactant partitioning between the aqueous surfactant solution and the crude oil to determine the partitioning influence on the adsorption results in the presence of crude oil. The mixture’s adsorption and partitioning behaviors were studied at a fixed salinity of 32 k ppm and temperatures of 80 and 106°C. High-performance liquid chromatography (HPLC) was used in measuring the surfactant concentration throughout adsorption and partitioning tests. Rock characterization was also performed in this study using X-ray diffraction (XRD) as well as X-ray photoelectron spectroscopy (XPS) before and after adsorption with and without crude oil being present. Static adsorption outcomes displayed the adsorption of APG, AEC, and the overall mixture with and without crude oil being present, because all are having a similar increasing trend when concentration increases. However, the adsorption values were much higher when crude oil was present as compared with the adsorption values when crude oil was absent; this is because of not considering the impact of surfactant partitioning. The adsorption values (i.e., at 0.2 wt%) for both temperatures were below 2.5 mg/g in the absence of crude oil and rose to around 3.5 mg/g in the presence of crude oil. A significant amount of what was adsorbed belongs to AEC because of its increased chain-chain interactions with APG, which was evidenced experimentally in our previous work; hence, AEC is the greatest contributor to the overall surfactant mixture’s adsorption. Also, temperature had an impact on the adsorption capacity of the AEC:APG mixture, showing that APG has a greater sensitivity to temperature in comparison to AEC. The adsorption behavior of APG was found to be the opposite of AEC, where the adsorption capacity at 106°C was lower for AEC than its adsorption capacity at 80°C and vice versa for APG. The surfactant partitioning results were used to validate the surfactant adsorption outcomes in the presence of crude oil. After eliminating the partitioning effect, the surfactant adsorption isotherms in both cases of the presence and the absence of crude oil were almost identical. The results highlighted the importance of measuring surfactant partitioning, and the impact that partitioning has on the total surfactant losses during the surfactant flooding process. XRD and XPS results indicated that the change of the rock structure after adsorption when crude oil was present was attributed to the rock dissolution phenomena. It was concluded that adsorption and partitioning take place in the water/oil/rock system simultaneously and taking that into account allows for the improved and proper designing of the surfactant flooding.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,005
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: Simulation ou modélisation
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,065
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0050,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,021
Tête enseignante GPT0,289
Écart entre enseignants0,268 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSimulation ou modélisation
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations28
Publié2022
Routes d'admission1
Résumé présentoui

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