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Enregistrement W4236252564 · doi:10.2118/2001-012

Determination of Production Operation Methods in Naturally Fractured Reservoirs

2001· article· en· W4236252564 sur OpenAlexafffund
Daoyong Yang, Q. Zhang, Yongan Gu

Notice bibliographique

RevueCanadian International Petroleum Conference · 2001
Typearticle
Langueen
DomaineEngineering
ThématiqueReservoir Engineering and Simulation Methods
Établissements canadiensUniversity of Regina
Organismes subventionnairesNatural Sciences and Engineering Research Council of Canada
Mots-clésProduction (economics)Petroleum engineeringComputer scienceGeology

Résumé

récupéré en direct d'OpenAlex

Abstract There are many naturally fractured reservoirs in the world, but few of them are optimally developed. In fact, it is difficult to characterize the naturally fractured reservoirs and predict the oil production, needless to mention the determination of appropriate production operation methods (POMs). Although there have been some formulas for evaluating well performance, a few were derived on the basis of production test data. In this paper, several general formulas are developed for evaluating inflow performance of both vertical wells and horizontal wells, based on the production test data obtained from three naturally fractured reservoirs. The influence of rock compaction and the inertial flow resistance in naturally fractured reservoirs are considered in these equations. Furthermore, theoretical models are also presented, into which reservoir engineering, production performance and surface facility performance are incorporated. These models are then applied to evaluate and determine the oil well POMs for two naturally fractured reservoirs. It has been shown from these two field applications that stable flowing performance, including its ceasing conditions, can be predicted. And artificial lift methods such as sucker-rod pumping can be efficient under certain reservoir conditions. The detailed field application results indicate that most of POMs determined from the theoretical models are technically feasible and economically viable. Introduction Naturally fractured reservoirs are found in all types of lithologies and throughout the geological stratigraphic columns. However, initial high oil rates have misled engineers in many instances to overestimate production forecasts of wells. Thus development of the naturally fractured reservoirs results in numerous economic failures(1). Meanwhile, field practices show that selection of appropriate production operation methods (POMs) is critical to the long-term profitability of most producing wells(2–8). An improper choice can not only substantially reduce production but also greatly increase operating costs. Once a type of POM has been determined to install on a producing well, usually the POM is unchanged, whether it was and still is the optimal choice under existing conditions. Therefore, It is essential that both accurate prediction of well inflow performance and appropriate selection of POMs be of great benefit to the optimal development of the naturally fractured reservoirs. In general, it is difficult to characterize the naturally fractured reservoirs, predict the oil production and further determine suitable POMs. The well inflow performance relationship (IPR), which represents the well's ability to produce fluids, is the first component to be considered in the process of selecting POMs(9). In the literature, although there have been some formulas for evaluating well performance, few were derived on the basis of production test data. Gubkina(10) presented a formula for evaluating vertical well inflow performance in the naturally fractured reservoirs, which was later improved by Bacnev et al.(11). However, the effect of well completeness on well inflow performance was not accounted for. To evaluate the horizontal well inflow performance in naturally fractured reservoirs, Joshi's formula(12) is modified to achieve better forecasts(13,14).

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: Simulation ou modélisation · Signal consensuel: Simulation ou modélisation
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,138
Score d'incertitude au seuil0,997

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,000
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 Opus0,026
Tête enseignante GPT0,313
Écart entre enseignants0,287 · 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

Citations1
Publié2001
Routes d'admission2
Résumé présentoui

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