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Enregistrement W2070082199 · doi:10.2118/09-03-10-tn

Role of Operating Practices on Performance of Waterfloods in Heavy Oil Reservoirs

2009· article· en· W2070082199 sur OpenAlexaboutno aff
A.K. Singhal

Notice bibliographique

RevueJournal of Canadian Petroleum Technology · 2009
Typearticle
Langueen
DomaineEngineering
ThématiqueReservoir Engineering and Simulation Methods
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésPetroleum engineeringBenchmarkingLead (geology)Water injection (oil production)Environmental scienceInjectorGeologyBusinessEngineering

Résumé

récupéré en direct d'OpenAlex

Abstract Identification of operating conditions that have been beneficial or injurious to overall performance of past waterfloods in heavy oil reservoirs is the first essential step towards optimization of similar projects in the future. Comparative analyses of performance of various waterfloods in a given region (deposited under similar conditions) can be very instructive to identify such conditions. Insights on performance of waterfloods in heavy oil reservoirs were derived from a comparative evaluation of recent performance history of three selected waterfloods. These waterfloods involved increasing, decreasing and steady water injection rates. It was seen that aggressive injection rates lead to increased oil rates, but at rapidly increasing water cuts. Decreasing water injection rates, on the other hand, lead to low oil rates, but with water cuts increasing relatively more gradually. There is, therefore, an economically optimal water injection rate strategy for each specific situation. Introduction In the current environment of volatile oil prices and economic uncertainties, there is a heightened need for reviewing the cost-effectiveness of ongoing waterflood and improved oil recovery (IOR) operations within the constraints of low incremental costs and risks. Some of the options being pursued include enhanced surveillance, benchmarking of performance against other successful projects in analogous reservoirs, intense characterization and simulation, upgrades to facilities, improvement in injection water quality and selective placement of additional injectors and producers. In viscous fingering-dominated waterfloods, aggressive water injection aggravates water channelling. High injection rates (sub-fracture) lead to high initial oil rates in the short-term, but they subsequently give rise to excessive water channelling which may result in loss of cost-effectiveness of the waterflood and, in some cases, premature abandonment. On the other hand, low injection-production rates (processing rates) in heavy oil waterfloods lead to relatively low oil rates, long payout periods and a long project life. There is, therefore, an optimal processing rate/strategy for each specific situation (based on technical and economic considerations) and, clearly, there is a need for systematically identifying it. Since the 1980s, over 70 waterfloods have been operated in the medium and heavy oil reservoirs of Alberta and Saskatchewan in Western Canada. Waterflooding in these reservoirs involves adverse mobility ratio and viscous fingering/water channelling leading to relatively early water breakthrough. Resulting requirements for handling large amounts of water poses formidable problems. This study was undertaken to explore whether a judicious scheduling of injection and production rates could improve cost-effectiveness of similar operations in the short- and long-term. We examined performance histories of several ongoing waterfloods, and three selected waterfloods are reviewed here. We did not have access to many details on these projects. We believe there is a persuasive case for our hypothesis. It is understood that the field data examined were, by no means, 'controlled' (i.e. there may possibly exist factors other than the ones we focused on in this review, and all cases may not strictly be comparable). Methodology We compared the performance of three selected waterfloods in the heavy oil reservoirs of Southern Alberta; namely, Jenner Upper Mannville O, Jenner Upper Mannville JJJ and Retlaw Mannville D8D.

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 candidatesaucune
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 enseignants0,223
Score d'incertitude au seuil0,424

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0030,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
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 Opus0,012
Tête enseignante GPT0,252
Écart entre enseignants0,240 · 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'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

Citations7
Publié2009
Routes d'admission1
Résumé présentoui

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