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Enregistrement W1990779003 · doi:10.2118/00-04-tn

Water Shut-off Treatments-Reduce Water and Accelerate Oil Production

2000· article· en· W1990779003 sur OpenAlexaboutno aff
F.B. Thomas, D.B. Bennion, G.E. Anderson, B.T. Meldrum, W.J. Heaven

Notice bibliographique

RevueJournal of Canadian Petroleum Technology · 2000
Typearticle
Langueen
DomaineEngineering
ThématiqueEnhanced Oil Recovery Techniques
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésWater cutPetroleum engineeringMicroscale chemistryProduced waterProduction (economics)Oil productionEnvironmental scienceOil fieldGeology

Résumé

récupéré en direct d'OpenAlex

Abstract Gel treatment applications have been used for production well WOR reduction. There have been a number of cases where conformance was very poor and, by invocation of gel treatment strategies, WOR was significantly reduced. This paper discusses both the characteristics of reservoirs and wells which result in high WOR, as well as characteristics of gel treatments which need to be designed in order to effectively minimize the produced water from a reservoir or an oil field. Examples are provided in this paper whereby, through the use of gel treatments in production wells, WOR and oil production was increased. The paper concludes that there is a significant upside to gel treatments for reservoir optimization and production well revitalization. Introduction A serious problem in oil-producing reservoirs is water production. As with most things in nature, fluids also tend to follow paths of least resistance which, in reservoirs, are often created by the heterogeneous nature of the rock. There are two levels to this heterogeneity. The first is microscale heterogeneity which could be represented as a simple porous feature distribution, and the second is macroscale heterogeneity which includes layering, natural or induced fractures, and high vertical and horizontal permeabilities. Both can lead to poor conformance and, therefore, need to be controlled. If conduits for water flow are available then they need to be blocked in order for production wells to continue operation. In terms of water disposal costs, approximately $1 billion is spent in Alberta alone each year. The macroscale heterogeneities are more commonly understood and more intuitive. It is commonly known that, in some instances where fracturing operations have been misapplied or resulted in unfortunate connections to bottom water sources, the fracture permeability (100 to 1,000 times greater than the permeability of the rest of the rock) has resulted in very quick water breakthrough and very low recovery of the hydrocarbons in the reservoir. A similar response can also be observed where high permeability layers are present in certain porous media. Nevertheless, their effect is that much of the rock remains unswept. Another form of macroscale heterogeneity, which contributes to very poor conformance is the case where poor cementing operations are present. In such cases, in order to produce anything from the well, near wellbore fluid profile modification must occur. The same applies for injection wells. For microscale conformance difficulties, often simple laboratory tests can identify problems associated with exploitation strategies. For instance, the recovery efficiency associated with a waterflood is often based on analogous reservoirs or past experience. In some cases, subtle changes in the structure of the rock can result in vast changes in the sweep associated with the flow unit even though the porosity remains about the same value. Many examples exist in the literature where permeability and porosity of reservoirs have been sufficiently high to motivate operating companies to full developmental strategies only to find out that, upon implementation, the sweep through the homogeneous flow unit is much less than the average literature numbers would have indicated.

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 enseignants0,733
Score d'incertitude au seuil0,546

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0020,000
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 Opus0,006
Tête enseignante GPT0,198
Écart entre enseignants0,191 · 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

Citations38
Publié2000
Routes d'admission1
Résumé présentoui

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