Distributed Fiber-Optic Sensors Characterize Flow-Control-Device Performance
Pourquoi ce travail est dans la base
Une base qui oublie comment elle a trouvé un travail ne peut pas être vérifiée. Voici les voies qui ont admis celui-ci.
Notice bibliographique
Résumé
This article, written by JPT Technology Editor Judy Feder, contains highlights of paper SPE 195869, “Characterization of Flow-Control-Device Performance With Distributed Fiber-Optic Sensors,” by Ben Banack, SPE, Halliburton; Lyle H. Burke, SPE, Canadian Natural Resources; and Daniel Booy, SPE, C-FER Technologies, et al., prepared for the 2019 SPE Annual Technical Conference and Exhibition, Calgary, 30 September–2 October. The paper has not been peer reviewed. The complete paper describes piloting the collection and analysis of distributed temperature and acoustic sensing (DTS and DAS, respectively) data to characterize flow-control-device (FCD) performance and help improve understanding of steam-assisted gravity drainage (SAGD) inflow distribution. Fiber-optic-based instrumentation was deployed within FCD-equipped active wells using permanently installed coiled tubing. Logs were performed on multiple wells during stable and transient flowing conditions. Additionally, acoustic recording using 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 flow-loop FCD acoustic characterization program is described. Introduction Installation of inflow control devices (ICDs) along SAGD production liners is common 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. Several FCD devices are commercially available for use in SAGD. Methodology In an effort to optimize FCD design and selection, a joint industry partnership (JIP) was formed and flow-loop testing conducted to establish FCD performance curves and erosion tolerance over wide pressure, temperature, and steam-quality ranges 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. These wells 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 DTS data. Although DTS is a common tool for optimizing SAGD production, it has certain limitations. Specifically, temperature changes along production wells typically do not allow a detailed definition or quantification of the inflow distribution along the wellbore. In addition to DTS, DAS was performed periodically on the FCD wells. DAS logging of SAGD producers has several potential uses, including flow profiling, steam breakthrough or noncondensable gas (NCG) detection, multiphase flow characterization, electric submersible pump (ESP) performance, completion failure analysis, and 4D seismic analysis. Although FCD characterization with DAS appears promising, a knowledge gap exists regarding how to move beyond qualitative analysis to quantitative analysis of FCD performance and the lateral emulsion inflow distribution. Pending satisfactory results, DAS logging on active wells potentially can 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 and an associated reduction in greenhouse gases.
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.
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 | 0,001 |
| 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 découle