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Enregistrement W4234916439 · doi:10.2118/2008-180

Is High-Pressure Air Injection (HPAI) Simply a Flue-Gas Flood?

2008· article· en· W4234916439 sur OpenAlexafffund
A.R. Montes, R.G. Moore, S.A. Mehta, M.G. Ursenbach, D. Gutierrez

Notice bibliographique

RevueCanadian International Petroleum Conference · 2008
Typearticle
Langueen
DomaineEngineering
ThématiquePlasma and Flow Control in Aerodynamics
Établissements canadiensUniversity of Calgary
Organismes subventionnairesNatural Sciences and Engineering Research Council of Canada
Mots-clésFlue gasEnvironmental sciencePetroleum engineeringFlood mythWaste managementEngineeringGeography

Résumé

récupéré en direct d'OpenAlex

Abstract High-Pressure Air Injection (HPAI) is an EOR process in which compressed air is injected into a deep, light-oil reservoir, with the expectation that the oxygen in the injected air will react with a fraction of the reservoir oil at an elevated temperature to produce carbon dioxide. Over the years, HPAI has been considered as a simple fluegas flood, giving little credit to the thermal drive as a production mechanism. The truth is that, although early production during a HPAI process is mainly due to repressurization and gasflood effects, once a pore volume of air has been injected the combustion front becomes the main driving mechanism. This paper presents laboratory and field evidence of the presence of a thermal front during HPAI operations, and its beneficial impact on oil production. Production and injection data from the Buffalo Field, which comprises the oldest HPAI projects currently in operation, were gathered and analyzed for this purpose. These HPAI projects are definitely not behaving as simple immiscible gasfloods. This study shows that a HPAI project has the potential to yield higher recoveries than a simple immiscible gasflood. Furthermore, it gives recommendations on how to operate the process to take advantage of its full capabilities. Introduction High-Pressure Air Injection (HPAI) is an emerging technology for the enhanced oil recovery of light oils that has proven to be a valuable process especially in deep, thin, low permeability reservoirs 1 -7. A number of successful high-pressure air injection projects in light oil reservoirs have been documented in the literature 8–10. Most of these projects have been operating for many years, attesting to their technical and economic success. The improvement in recovery of light oil by HPAI involves a combination of complex processes, each contributing to the overall recovery. These processes include: flue gas sweeping, field re-pressurization, oil swelling, viscosity reduction, stripping of the lighter components of the oil, and thermal effects. Early production during the HPAI process is related to re-pressurization and gasflood effects; hence, the influence of the thermal zone is secondary during the early life of an injector. The oil displaced directly by the combustion front will depend on the effectiveness of the generated flue gas on oil displacement from outside the thermal region. For many years, there has been some discussion regarding the effective driving mechanisms associated with the HPAI process; some authors have assumed it is essentially attributable to the in-situ generated flue gas displacement and consequently the process is analogous to a flue-gas injection, while others recognize the thermal nature of the process. Clara et al.11, explained the air injection technique applied to light-oil reservoirs, and proposed a laboratory strategy for evaluation of an air injection project. It was stated that regardless of the oxidation zones, the air injection process in a light oil reservoir is comparable to a flue-gas injection process. Hunedi et al.12, presented results of an exhaustive EOR screening based on successful field trials and physics of the oil recovery mechanisms for each method; with the possibility to be applied in eight oil fields (30.2 to 41.3 ° API) in the Euphrates Graben.

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)0,000
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: Simulation ou modélisation · Signal consensuel: Simulation ou modélisation
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,061
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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 Opus0,010
Tête enseignante GPT0,192
Écart entre enseignants0,182 · 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

Citations4
Publié2008
Routes d'admission2
Résumé présentoui

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