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Enregistrement W2806609338

Development of scaling criteria and numerical simulation study of steam flooding process

2018· dissertation· en· W2806609338 sur OpenAlexfundaboutno aff
Arifur Rahman

Notice bibliographique

RevueMemorial University Research Repository (Memorial University) · 2018
Typedissertation
Langueen
DomaineEngineering
ThématiqueEnhanced Oil Recovery Techniques
Établissements canadiensnon disponible
Organismes subventionnairesNatural Sciences and Engineering Research Council of CanadaMemorial University of Newfoundland
Mots-clésScalingDimensionless quantityPetroleum engineeringProcess (computing)Flooding (psychology)Porous mediumDisplacement (psychology)Enhanced oil recoveryFlow (mathematics)Fluid dynamicsPorosityGeologyComputer scienceMathematicsGeotechnical engineeringMechanicsGeometryPhysics
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Canada contains reserves of oil sand and heavy oil resources considered to be the largest amount of unconventional hydrocarbons deposited in unfavorable conditions. It needs more efforts and technological advancement to recover oil from such reserves. Steam flooding enhanced oil recovery technique is applied for more than 70% of heavy oil reservoirs to extract the oil. Threedimensional (3D) displacement model can represent an appropriate approach and model for the steam flooding process. However, their physical limitations make it impossible to duplicate the real behavior of a reservoir in larger scale. So, it is important to develop scaling criteria for depicting the actual fluid behavior for unconventional reservoirs. Scaled physical models have the unique advantage of capturing all physical phenomena occurring in a particular process by transforming the parameters into dimensionless numbers. This concept is applicable to fluid flow through porous media, where continuous alteration of rock and fluid properties can be characterized by various dimensionless numbers. In this study a set of dimensionless groups were developed using both inspectional and dimensional analyses. The new groups of dimensionless numbers can be used to characterize the reservoir rock and fluid properties for better explanation of complex rock/fluid phenomena for the steam flooding process. It should be noted that the complete set of scaling criteria is very difficult to satisfy. Therefore, some of the similarity groups must be relaxed in order to satisfy the most important parameters of the specific reservoir activities. The choice of which requirements to relax depends on the particular process being modeled. Scaling of the phenomena considered to be least important to a particular process might be relaxed without significantly affecting the major features of the process. The choice of an approach depends on the importance of the phenomena that are not scaled by that approach. Major scaling groups were found by applying different elimination techniques. The effect of those dominant dimensionless groups on recovery was evaluated through the study of process controlling parameters. A new group which is called Dykstra-Parsons coefficient is introduced to incorporate the reservoir heterogeneity. A combined dimensionless group was proposed to characterize and evaluate the performance and found to have the largest effect on oil recovery. Sensitivity analysis of scaling numbers is performed to find out the relative effect of each dimensionless numbers on oil recovery. Finally, a numerical simulation study is performed to quantify the effect of steam quality and permeability variations for different reservoirs. This research work leads to the development of a procedure that can be applied to design a steam flooding EOR process. This process allows the assessment of different parameters to aid in the selection of optimum additive concentration to account for the uncertainties due to reservoir heterogeneity. The process is flexible; it can be applied to wide range of reservoir types as there exists a physical commonality between laboratory and field scale.

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)0,001
score de la tête « metaresearch » (Gemma)0,000
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: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,131
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0020,001
Études des sciences et des technologies0,0010,000
Communication savante0,0000,001
Science ouverte0,0010,000
Intégrité de la recherche0,0010,001
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 Opus0,029
Tête enseignante GPT0,308
Écart entre enseignants0,279 · 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'étudeExpérimental (laboratoire)
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

Citations1
Publié2018
Routes d'admission2
Résumé présentoui

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