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Enregistrement W2031402312 · doi:10.2118/2004-265

Characterization and Role of Reservoir Heterogeneity in Performance of Infill Wells in Water Flood and Miscible Projects

2004· article· en· W2031402312 sur OpenAlexaboutno aff
A.K. Singhal, S.J. Springer

Notice bibliographique

RevueCanadian International Petroleum Conference · 2004
Typearticle
Langueen
DomaineEngineering
ThématiqueHydraulic Fracturing and Reservoir Analysis
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésInfillFlood mythReservoir modelingPetroleum engineeringCharacterization (materials science)GeologyGeotechnical engineeringEngineeringCivil engineeringMaterials scienceArchaeologyGeography

Résumé

récupéré en direct d'OpenAlex

Abstract Reservoir heterogeneities are known to influence performance of infill wells in water flood or miscible flood projects to the extent of determining their success or failure, but no satisfactory ways currently exist for their characterization, quantification or prediction. In 1950, Dykstra and Parsons1 presented empirical correlations for performance of water floods in certain California reservoirs, based on observed variation in permeability. In general, procedures for most reservoir engineering predictions could be greatly simplified if heterogeneity could be characterized by one or more terms. However, it is painfully obvious that because of the multitude of reservoir descriptions encountered in any oil prone basin, a single parameter (or a small number of parameters) may not be adequate. This is because of different ways in which heterogeneity impacts the performance of vertical or horizontal infill wells placed to enhance production/ reserves in a variety of water flood/ miscible flood situations. In recent years, a large number of infill wells were placed in various water flood and miscible flood projects in the Western Canadian Sedimentary Basin (WCSB). Performance of both horizontal and vertical infill wells varied widely in terms of initial oil rates and reserves. They also resulted in mixed economic successes in that many of these infill wells were unable to payout. In general, horizontal infill wells drained about twice the amount of oil as contemporaneous vertical infill wells placed in the same reservoirs. However, in spite of this, they may sometimes be less attractive on a risk-weighted basis, because of their higher cost. It is postulated that reservoirs are divided into predominantly horizontal or vertical 'compartments' due to spatial variations in reservoir attributes such as permeability, thickness, environment of deposition (stratification, shaliness) and post-depositional changes (structure, fractures, digenesis). Oil contained in these compartments, though often not strictly isolated, is not easily contacted or displaced by injected water/ gas. Alternately, flow within the reservoir might be predominantly through certain pathways, some of which might involve cross-flow between various intervals. Thus horizontal wells will do a better job of draining oil than vertical wells in certain situations and, vice-versa. However, in reservoirs with certain kinds of heterogeneity, distribution of mobile water and/or gas in an ongoing water flood or miscible flood project might make horizontal wells more vulnerable to prematurely watering/ gassing out and hence, more risky. This paper presents a few case studies of infill wells in water flood and miscible flood projects in WCSB. Comparative performance is presented for infill horizontal and vertical wells placed in these projects during the past fifteen years. Through a review of statistical data, certain observations are made regarding heterogeneity and its relation to performance of horizontal wells. Finally, inferences are generalized to obtain clues to situations where horizontal or vertical infill wells may be more appropriate. Introduction In water flood or EOR recovery projects, recovery at any point in time is a product of displacement efficiency, conformance (vertical sweep efficiency) and areal sweep efficiency. Whereas conformance could be estimated using Dykstra-Parson's procedures1, sweep efficiency largely depends on areal heterogeneity in different intervals, besides factors such as mobility ratio, injected fluid throughput and flood pattern geometry2

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: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,302
Score d'incertitude au seuil0,976

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)0,0000,000

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 GPT0,195
Écart entre enseignants0,187 · 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'étudeObservationnel
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
Publié2004
Routes d'admission1
Résumé présentoui

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