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Enregistrement W1966653694 · doi:10.2118/2007-055

Do Heavy and Medium Oil Waterfloods Differ?

2007· article· en· W1966653694 sur OpenAlex

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Notice bibliographique

RevueCanadian International Petroleum Conference · 2007
Typearticle
Langueen
DomaineEngineering
ThématiqueReservoir Engineering and Simulation Methods
Établissements canadiensSaskatchewan Research Council (Canada)
Organismes subventionnairesPetroleum Technology Research Centre
Mots-clésCitationPetroleumPetroleum engineeringPetroleum industryComputer scienceEnvironmental scienceLibrary scienceEngineeringGeologyEnvironmental engineering

Résumé

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Abstract To identify the parameters which impact heavy oil waterflood success, we collected production, reservoir, and operating data for 83 western Canadian waterfloods. The waterfloods were classified as either heavy or medium, and separate multivariate analysis models were built for each set. The differences between waterflooding of heavy oils and their medium oil counterparts were substantial and revealing: In terms of operational parameters, incorporating horizontal and directional wells, both producers and injectors, was significantly important to the success of heavy oil waterfloods, but insignificant for medium oils. The two most important reservoir parameters affecting the success of waterflooding medium oils permeability and heterogeneity were insignificant for heavy oils. Introduction Waterflooding is the most common method of enhancing oil production, and is becoming increasingly important in recovering heavy oil. Of the 5201 million m3 of heavy oil in place in Alberta and Saskatchewan, 207 waterflood operations (including 8 abandoned waterfloods) recover more than 24% of that oil in place. Some of the western Canadian heavy oil waterfloods were highly successful, recovering as much as seven times the primary recovery. Other waterfloods fared less well ? eight waterfloods were abandoned in the Saskatchewan Lloydminster region. Despite its prevalence, little is known about how waterflooding heavy oils differs from waterflooding their lighter oil counterparts. There is a substantial body of work on designing, monitoring, and managing waterfloods: however, the problems specific to producing heavy oil by waterflooding are rarely addressed. Some exceptions include five case studies of heavy oil waterfloods, including Forth et al.'s statistical study identifying important parameters in the Golden Lake waterflood.1-5 Two more general studies were Smith's paper on mechanistic aspects of heavy oil waterflooding and Miller's review of the state of the art of waterflooding technology as applied to western Canadian heavy oils.6,7 Miller discussed performance prediction and problems, and offered recommendations to improve performance. In contrast to the case studies, the Saskatchewan Research Council (SRC) performed two statistical studies on a group of heavy oil waterfloods.8,9 Univariate and multivariate analysis were used to highlight the broad themes common to heavy oil waterfloods. Such an approach requires a numerical value corresponding to success, and the SRC has now tested seven such measures. Other authors have also used statistics to assess waterfloods, albeit those producing lighter oils than the western Canadian sites examined in this study. The data mining study of Weiss et al. tested the ratio of secondary production to primary production to analyze Nebraska waterfloods.10 Wu et al. unsuccessfully tried to correlate reservoir parameters with the recovery of 24 west Texas waterfloods.11 McLachlan and Ershagi equated the efficiency of waterfloods to cumulative water/oil ratio.12 Methodology The reservoir data were obtained from documents published by the two provincial regulatory bodies: Saskatchewan Industry and Resources' Reservoir Annual 2002, and Alberta's Energy and Utilities Board (EUB) 2003 Statistical Series.13,14 The production data were obtained from Accumap™.15 Many waterfloods were excluded from the study. We wanted to attribute differences in recovery to the effects of waterflooding, and so excluded operations which had previously used other enhanced oil rec

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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: Simulation ou modélisation · Signal consensuel: Simulation ou modélisation
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,223
Score d'incertitude au seuil0,589

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 Opus0,018
Tête enseignante GPT0,254
Écart entre enseignants0,236 · 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