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Enregistrement W2044559513 · doi:10.2118/02-12-03

Determination of Production Operation Methods in Naturally Fractured Reservoirs

2002· article· en· W2044559513 sur OpenAlexafffund
Daoyong Yang, Yongan Gu, Qinqin Zhang

Notice bibliographique

RevueJournal of Canadian Petroleum Technology · 2002
Typearticle
Langueen
DomaineEngineering
ThématiqueOil and Gas Production Techniques
Établissements canadiensPetroleum Technology Research CentreUniversity of Regina
Organismes subventionnairesPetroleum Technology Research CentreUniversity of Regina
Mots-clésPetroleum engineeringArtificial liftInflowGeologyCompactionReservoir engineeringOil fieldProduction (economics)Sucker rodReservoir modelingOil productionGeotechnical engineeringPetroleum

Résumé

récupéré en direct d'OpenAlex

Abstract There are many naturally fractured reservoirs in the world, but few of them are developed optimally. In fact, it is difficult to characterize naturally fractured reservoirs and predict their oil production, let alone determine their appropriate production operation methods (POMs). Although there exist some formulas for evaluating well performance, few are derived on the basis of production test data. In this paper, several general formulas are developed to evaluate inflow performance of both vertical and horizontal wells, based on the production test data of three naturally fractured reservoirs. The rock compaction and the inertialflow resistance in the naturally fractured reservoirs are taken into account in these equations. Furthermore, theoretical models are presented to consider reservoir engineering, production performance, and surface facility performance. These models are then applied to evaluate and determine the POMs for two naturally fractured reservoirs. These two field applications show that stable flowing performance can be predicted accurately, and that artificial lift methods, such as sucker-rod pumps, can be mployed efficiently under certain reservoir conditions. The detailed field application results indicate that most of the POMs, as suggested by 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, the initial high oil rates seen in these reservoirs have misled petroleum engineers, in many instances, to overestimate their future production performance. Thus, the development of naturally fractured reservoirs has resulted in numerous economic failures(1). Meanwhile, field practices show that the 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 oil production, but also greatly increase operating costs. Once a POM is employed in a producing well, usually this POM remains unchanged, regardless of whether it will still be the optimal choice under subsequent conditions. Therefore, both the accurate prediction of well inflow performance and the appropriate selection of POMs are of great benefit to the optimal development of naturally fractured reservoirs. In general, it is difficult to characterize naturally fractured reservoirs, predict their oil production, and further determine suitable POMs. The well inflow performance relationship (IPR), which represents the well deliverability to produce fluids, is the first component to be considered in the process of selecting POMs(9). In the literature, although there are some formulas available for evaluating well performance, few are derived on the basis of production test data. Gubkina(10) presented a formula for evaluating the vertical well inflow performance in naturally fractured reservoirs, which was later improved by Bacnev et al.(11) However, the effect of well completion on well inflow performance was neglected. Joshi(12) and Karcher et al.(13) developed models to evaluate the horizontal well inflow performance in naturally fractured reservoirs. Mullane et al.(14) improved Joshi's method to achieve better forecasts. Other formulas(15–17) were developed, in which several unknown quantities are difficult to obtain from oil fields.

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 enseignants0,812
Score d'incertitude au seuil0,774

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0040,001
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 Opus0,012
Tête enseignante GPT0,258
Écart entre enseignants0,246 · 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

Citations3
Publié2002
Routes d'admission2
Résumé présentoui

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