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Enregistrement W2056015416 · doi:10.2118/05-02-01

Analysis of Immiscible Water-Alternating-Gas (WAG) Injection Using Micromodel Tests

2005· article· en· W2056015416 sur OpenAlexaff
Mingzhe Dong, J. Foraie, Ioannis Chatzis

Notice bibliographique

RevueJournal of Canadian Petroleum Technology · 2005
Typearticle
Langueen
DomaineEngineering
ThématiqueEnhanced Oil Recovery Techniques
Établissements canadiensUniversity of WaterlooSaskatchewan Research Council (Canada)University of Regina
Organismes subventionnairesNorthwestern University
Mots-clésMicromodelPetroleum engineeringResidual oilWater injection (oil production)Fossil fuelPermeability (electromagnetism)Enhanced oil recoveryGeologyEnvironmental scienceWaste managementPorous mediumChemistryGeotechnical engineeringPorosityEngineering

Résumé

récupéré en direct d'OpenAlex

Abstract In waterflooded reservoirs, it is possible to recover a significant amount of residual oil by enhanced oil recovery. Immiscible water-alternating-gas (WAG) injection is one of the well-established methods for improving oil recovery. However, the mechanism of three-phase flow in the process has not been well understood and prediction of the three-phase permeability has been highly uncertain. This paper presents the results of immiscible WAG injection in a water-wet micromodel. During immiscible gas injection after an initial waterflood, gas moved through the residual oil paths, and residual oil was pushed either toward the production end of the model or into previously waterflooded channels. Breakthrough of gas occurred at about 0.25 PV for the micromodel used in this work. Further gas injection beyond the breakthough volume increased oil recovery only very slightly. When water was injected following gas injection, it flowed through channels that were created in the initial waterflood. Most of the residual oil that had been pushed into these waterflooded channels by the previous gas injection was produced. The mechanism of gas, oil, and water flow during immiscible WAG injection was analyzed. The observations and analysis provide insight into the flow behaviour of a three-phase system in the immiscible WAG process, which is important in the modelling of the process. Introduction A problem with gas injection (both miscible and immiscible) is the inherently unfavourable mobility ratio and the resulting poor volumetric sweep in reservoirs. Injection of gas as slugs alternated with water slugs, or water-alternating-gas (WAG), is the common practice presently used for controlling gas mobility. The WAG technique is indeed a combination of two oil recovery processes: gas injection and waterflood. However, the use of the combination of the two processes has resulted in some problems that have perplexed the industry since the pilot test studies were implemented in the early 1970s. In the immiscible gas injection process, the portion of the injected gas dissolved in the oil reduces the oil viscosity. In addition to reducing viscosity, the dissolved gas also swells the oil, so for a given fixed residual oil saturation, less stock tank oil remains after a waterflood. These two mechanisms have been demonstrated by numerous laboratory PVT and coreflood tests. Laboratory coreflood experiments also showed that the free gas displacement is a very important mechanism for immiscible gas injection. Analysis of results from a tertiary CO2 injection field test revealed that incremental oil production by immiscible CO2 injection has two components(1). The first is an instantaneous response, probably resulting from gas displacing oil that was not being displaced by water. The second component is the long-term effect caused by viscosity reduction, swelling, and relative permeability alteration. The mechanism of instantaneous response, i.e., a sharp increase in the oil production rate during a CO2 slug injection, is still not well understood. Spival et al.(1) also realized that N2 contained in the CO2 stream is a complicating factor that reduces the solubility of CO2 in the oil and, on the other hand, may decrease the residual oil saturation by being trapped in the reservoir.

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 candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,365
Score d'incertitude au seuil0,901

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0100,002
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)0,0000,000

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 Opus0,008
Tête enseignante GPT0,225
Écart entre enseignants0,217 · 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.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
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

Citations71
Publié2005
Routes d'admission1
Résumé présentoui

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