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Enregistrement W1971966656 · doi:10.2118/0114-0069-jpt

Simulation of Flow-Control Devices With Feedback Control for Thermal Operations

2014· article· en· W1971966656 sur OpenAlexaboutno aff
Adam Wilson

Notice bibliographique

RevueJournal of Petroleum Technology · 2014
Typearticle
Langueen
DomaineEngineering
ThématiqueReservoir Engineering and Simulation Methods
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésSteam-assisted gravity drainageInflowSteam injectionPetroleum engineeringWellboreGeologyHydrology (agriculture)EngineeringAsphaltGeotechnical engineeringOil sandsArchaeology

Résumé

récupéré en direct d'OpenAlex

This article, written by Special Publications Editor Adam Wilson, contains highlights of paper SPE 163594, ’Advanced Wellbore Simulation of Flow-Control Devices With Feedback Control for Thermal Operations,’ by Terry Stone, SPE, Schlumberger Information Solutions; Carlos Emilio Perez Damas, Schlumberger Calgary Regional Technology Centre; Glenn Woiceshyn, SPE, Absolute Completion Technologies; David Hin-Sum Law, SPE, Schlumberger Calgary Regional Technology Centre; George Brown, Schlumberger Fibre Optics Technology; and Peter Olapade and William J. Bailey, SPE, Schlumberger-Doll Research, prepared for the 2013 SPE Reservoir Simulation Symposium, The Woodlands, Texas, USA, 18-20 February. The paper has not been peer reviewed. Factors such as hydraulic gradients in the horizontal completion, geologic and fluid variations in the reservoir, and well-placement issues can produce very poor steam conformance in steam-assisted gravity drainage (SAGD). Using proportional-integral- derivative (PID) feedback to control steam injection can lead to improvements in SAGD. Inflow- or injection-control devices (ICDs) can also improve SAGD performance. This paper examines detailed wellbore simulations of a SAGD process in which wells are equipped with a combination of ICD completions and feedback control to determine the physical mechanisms and outline practical procedures to determine an improved ICD completion and feedback-control design. Introduction SAGD is the most extensively used process for development of the bitumen resources in western Canada. Fig. 1 shows the concept of this process in which two closely spaced horizontal wells are placed such that the upper well injects steam and the lower producer collects reservoir fluids that drain mostly by gravity from a constantly evolving steam chamber. Ideally, the steam chamber evolves uniformly along the entire length of the well pair. However, the actual pattern of SAGD well pairs shows very irregular steam-chamber development along the lengths of most of the pairs in the pattern. For many years, SAGD operators in this region have been evaluating methods to improve conformance along well pairs. One such method is to install dual- tubing strings in the injector and producer. Prescribed injection and production rates from each tubing string may be determined from a reservoir-engineering analysis of the formation around the pair, but such analyses are often inexact. In order to prevent the steam chamber from touching the lower producer, which would then remove hot steam instead of using it more efficiently in the upper reaches of the chamber, the injection and production rates are usually set to maintain a prescribed temperature difference between fluids exiting the upper injector and entering the lower producer. This temperature difference, also referred to as a “subcool” because it is set to be several degrees below a water saturation temperature, may be controlled at both the heel and the toe of the well pair by use of the ability to inject and produce from the two tubing strings that are landed at these points. However, setting these injection and production rates to reflect the current state of the reservoir and current subcool is difficult with conventional reservoir-engineering analysis. To improve on this, a proposal was made to use a feedback controller to monitor temperatures of produced and injected fluids automatically, with a target subcool at the heel and the toe of the well pair. By targeting the same subcool at the heel and the toe, two objectives are accomplished: (1) achieving the subcool prevents steam from entering the lower producer, and (2) both toe and heel halves are encouraged to produce uniformly because both are targeting the same subcool (i.e., if one-half temporarily operates at a lower or higher subcool than the target, steam injection is decreased or increased, respectively, in that half to compensate). Another method to improve conformance is to install carefully constructed ICDs in either the injector or the producer, or in both. When placed in the injector, these devices can equalize the outflow of steam from heel to toe better, regardless of variations in reservoir mobility properties.

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: aucune
Score de désaccord entre enseignants0,567
Score d'incertitude au seuil0,388

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 Opus0,007
Tête enseignante GPT0,246
Écart entre enseignants0,238 · 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
Publié2014
Routes d'admission1
Résumé présentoui

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