Characterization of Flow Control Device Performance with Distributed Fiber-Optic Sensors
Notice bibliographique
Résumé
Abstract It is becoming common to install inflow control devices (ICDs) along steam-assisted gravity drainage (SAGD) production liners to enhance temperature conformance and accelerate depletion. Additionally, some operators advocate the installation of similar outflow control devices (OCDs) along the injection well of the SAGD well pair. Collectively, these inflow and outflow devices are often referred to as FCDs. Industry adoption of flow control devices (FCDs) has increased, and several devices are commercially available for use in SAGD. In an effort to optimize FCD design and selection, a joint industry partnership (JIP) was formed (Vachon et al. 2015) and flow loop testing conducted to establish FCD performance curves and erosion tolerance over a wide range of pressure, temperature, and steam quality (SQ) consistent with a typical SAGD well environment. In conjunction with flow loop testing, several full-scale FCD deployments were completed at the JIP fields, including pilot wells at the production company's SAGD facility, which were logged with fiber-optic technology. Fiber-optic-based instrumentation was deployed within FCD-equipped wells using permanently installed coiled tubing. Well architecture design changes to a typical completion were not required because fiber-optic sensors are used for most non-FCD wells to collect distributed temperature sensing (DTS) data. Although DTS is a common tool for optimizing SAGD production, it has certain limitations; specifically, temperature changes along production wells do not typically allow a detailed definition or quantification of the inflow distribution along the wellbore. In addition to DTS, distributed acoustic sensing (DAS) was periodically performed on the FCD wells. DAS logging of SAGD producers has several potential uses, including flow profiling, steam breakthrough and/or noncondensable gas (NCG) detection, multiphase flow characterization, electric submersible pump (ESP) performance, completion failure analysis, and four-dimensional seismic analysis. Although FCD characterization with DAS appears promising, a knowledge gap exists as to how to move beyond qualitative analysis to more quantitative analysis of FCD performance and the lateral emulsion inflow distribution. Pending satisfactory results, DAS logging on active wells can potentially be completed to accelerate improvements of SAGD FCD performance and design as well as increase the efficiency of SAGD recovery through improved steam/oil ratio (SOR) and an associated reduction in greenhouse gases. This paper describes piloting the collection and analysis of DTS and DAS data to help improve understanding of SAGD inflow distribution. Logs were performed on multiple wells during stable and transient flowing conditions. Early surveillance demonstrated suitability and limitations of fiber-optic-based logging to validate FCD performance in active wells. In addition to field logging, acoustic recording using JIP flow loop testing was completed with accelerometers, geophones, and fiber-optic cables during FCD characterization. The goal was to cross reference the acquired acoustic signals for quantification of flow at devices and validation of performance. An overview of the JIP flow loop FCD acoustic characterization program is described.
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 enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».