Application of Polymer and Nanomaterials for Improving Heavy Oil Recovery
Notice bibliographique
Résumé
Booming population growth and economic activity have contributed significantly to an increased demand for energy in the last few decades, specifically in Canada. A major source of energy is oil extracted from underground petroleum reservoirs. Utilizing current technology and equipment, only a small portion of oil can be produced and recovered. Steam-assisted gravity drainage (SAGD), used as a common technique to produce heavy oil in Canada (specifically in oil sands reservoirs), requires a lot of energy and negatively impacts the environment. Using environmentally friendly and cost-effective techniques instead of or combined with SAGD improves the extraction of oil from Canadian oil reservoirs. Reservoir pressure, which is a driving force for pushing oil toward production wells, reduces drastically in the early stages of oil production from underground resources. This leads to a significant decrease in oil production rate. To solve this problem, enhanced oil recovery (EOR) methods inject water, gas, or chemical solutions to maintain reservoir pressure. When water is injected (water flooding) into heavy oil reservoirs, it cannot push the viscous oil smoothly because of water’s lower viscosity as compared to oil. As a result, injected water tends to bypass the pores containing trapped oil and the flooding becomes inefficient. To overcome this problem, one method adds polymers to the injected water. The addition of polymers leads to a more uniform flooding by increasing the viscosity of the injected fluid. Unfortunately, this approach suffers seriously from degradation of polymers at high temperatures and precipitation of polymers due to interaction with ions like sodium and calcium in brine. To solve these problems, the addition of nanomaterials to a polymer solution is highly recommended. The main focus of this PhD dissertation is to evaluate the effect of surface chemistry and geometry of nanomaterial on creation of a network with large polymer molecules. In addition, to mimic the large deformations in converging and diverging pores in porous media, linear and nonlinear rheology were employed to characterize the mechanical and flow behaviors of these hybrid dispersions. Sandpacks were used as the porous media to simulate oil reservoirs. Different hybrid dispersions were injected into sandpacks and the yield of recovered oil was reported. Results of this work can pave the way for use of polymer/nanomaterial solutions for heavy oil recovery. This study also demonstrated that large deformation oscillatory shear tests can be employed to distinguish flow behavior of hybrid systems. It was also shown that interaction between polymer and nanomaterial affects network structure and, consequently, oil recovery. Furthermore, size of nanomaterial compared to pore size distribution of porous media is a significant parameter that should be considered. The outcomes of this study could be helpful in improving heavy oil recovery in thin oil formations such as the Cardium, Montney, and Ostracod formations. These formations are too thin to utilize processes like steam-assisted gravity drainage and solvent vapor extraction, making this technique significant for increasing oil recovery in Canadian heavy oil reservoirs.
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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 | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
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 ».