Experimental Study of Surfactant Flooding on Organic Shale with Integrated Characterization
Notice bibliographique
Résumé
Abstract Enhanced oil recovery from organic shale reservoirs has increasingly gained interest from oil and gas industry in recent years. The recovery factor of organic shale oil production depends on formation wettability and pore fluid trapping mechanisms. A combination of hydraulic fracturing and surfactant flooding can be used to reduce oil trapping and increase oil recovery by reducing the interfacial tension and decreasing oil wettability. A novel experimental workflow has been developed based on fluid flow monitoring and NMR characterization to study the effect of surfactant flooding on organic-rich shales in the lab. Two blends of surfactants (cationic and nonionic) were carefully selected from prior contact angle (CA) and interfacial tension (IFT) measurements for the surfactant flooding tests. Micro-CT screening was used to select fracture-free samples for these tests. Prior to flooding we acquired nuclear magnetic resonance (NMR) T1-T2 measurements on as-received core samples to establish base-line water and oil saturations. Next, the core samples were pressure-saturated with crude oil at reservoir pressure and temperature, and we continued the aging process for a given time. Following aging, core samples were flooded using continuous crude oil injection from one end of the core sample whilst monitoring fluid flow rate, temperature, and pressure. Robust initial effective oil permeability was computed when the flow system reached steady state. Next, fracturing fluids -with and without surfactants- were injected from the opposite end of the core plugs to simulate the forced imbibition of fracturing fluid along with hydraulic fracturing in real field operations. Finally, the injection of crude oil was resumed from the original end of the core sample to establish the flowback effective oil permeability after hydraulic fracturing and surfactant flooding. We acquired NMR data after each fluid injection step to monitor fluid saturation and wettability changes in the core samples. Additionally, porosity and saturation measurements, X-ray diffraction (XRD), rock-eval pyrolysis and mercury injection capillary pressure (MICP) tests are performed to characterize fluid distribution, mineralogy and pore throat sizes of the rock samples. The results of fracturing fluid injection in all core samples clearly indicate that the water from the fracturing fluid does partially displace the crude oil in the core, effectively making this oil recoverable. Samples injected with the blend of cationic surfactants show less than 3% incremental recovery over samples with no surfactant injection. The flowback effective oil permeabilities of all core samples are much lower than the initial effective oil permeabilities prior to fracturing fluid injection. This observation is corroborated by the differences in MICP results before and after fracturing fluid injection, showing smaller pore throat sizes after fracturing fluid injection. Our novel workflow has successfully characterized the impact of surfactant flooding on organic-rich shale samples in lab-scale tests. and can be used for screening of surfactant enhanced oil recovery before running more expensive field trials.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| 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 | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».