Visual Analysis of SAGD with Chemical Additives
Notice bibliographique
Résumé
Abstract SAGD (steam assisted gravity drainage) has been proven as an effective technology to enhance heavy oil/bitumen recovery. The main shortcoming of this method is its inefficiency, a result of high water and energy consumption. As a solution to SAGD efficiency improvement, we propose the addition of chemicals resulting in higher recovery and reduced steam consumption. The objective of this paper is to screen new generation chemicals as additives and study the mechanisms and optimum injection strategies. This screening was achieved through Hele-Shaw type macroscopic visual experiments. We previously screened a wide variety of chemical additives (Bruns and Babadagli 2017, 2018) for steam flooding. As a continuation of this work, these chemicals were tested for SAGD conditions using a new visual experimental design where the optimal injection strategies were identified, eventually providing a reference for the selection of chemical additives for field applications. 11 conventional and new generation chemical additives (heptane, biodiesel, DME, LTS-18, Tween 80, Span 80, Novelfroth 190, ionic liquid (BMMMIM BF4), silicon dioxide nanoparticle, DES9, and DES11) were selected based on both their strong thermal stability and enhanced oil recovery capability. The recovery improvement mechanisms for the different chemical additives and different injection strategies were identified through flow characteristics, emulsifying ability, viscosity reduction capability, and wettability alteration. Simultaneously, the mechanisms were studied from a macro perspective via analyzing areal sweep efficiency and microscopic oil displacement efficiency together with observing the images acquired during the process. Three different injection strategies were applied for each chemical: (1) chemicals were injected at the beginning, (2) in the middle, and (3) at the end of the steam injection. The chemical additives played different roles in recovery improvement, and different chemical addition strategies yielded different mechanisms. Heptane exhibited extraordinary characteristics with maximum "steam saving" (34.52%) when the middle injection strategy was applied and maximum ultimate oil recovery (64.75%) was obtained for the end injection strategy due to the ability to reduce the viscosity of heavy oil by dissolving around the chamber edge. Steamflooding with Novelfroth 190 showed an excellent performance for the middle and end injection strategies due to its ability to develop rapid oil drainage "channels". The addition of surfactant LST-18 presented the ability to improve the EOR by forming emulsions. Additionally, the distributions of the steam chamber in the Hele-Shaw cell were different due to the changed flow characteristics when the same chemical additive was injected at different times, thus showing the ability to reduce viscosity and form emulsions with different strengths.
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 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 | 0,001 | 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) | 0,002 | 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 ».