Application of Intelligent System (DES PCP) For Monitoring Progressing Cavity Pumps
Notice bibliographique
Résumé
Abstract In March 2002 REDUCT &Lobbe completed a comprehensive field research project for the Saskatchewan Petroleum Technology Research Center (SPTRC). This research project addressed the potential application of intelligent systems for heavy oil production in Western Canada. The objectives of the tests were to:assess the opportunities and identify application areas for intelligent systems;develop and demonstrate a specialized, site-specific intelligent system; andassess the performance and potential benefits of the implemented system. Six applications were identified as candidates for AI systems, and Progressing Cavity Pumps (PCPs) monitoring, diagnosis and control was selected for demonstration tests. The overall conclusions from the tests were that intelligent systems have the ability to:diagnose well instabilities that a SCADA system alone cannot recognize; notify operators about wells that require closer scrutiny (review);provide decision support with respect to recovery from different types of failures. These benefits are critical in terms of optimizing PCP operations and retaining and making available the knowledge of experienced staff. The value of the potential benefits of a PCP intelligent system was estimated at up to $ 4,000 per well per year, or several million dollars per year for a mid-size PCP operation. Given that the cost of the software represents only a fraction of SCADA infrastructure expenditures, the implemented intelligent system could significantly leverage the SCADA system in management and operations of PCP wells. Introduction Conventional control and automation helps prevent costly shutdowns, increases yield and quality, and results in more effective and efficient operation of the process equipment. However, automation of some production aspects is often not easy because oil-producing operations exhibit complex interactions. To derive better performance and to supplement conventional control technologies with more advanced features, the oil industry has turned, therefore, to advanced IT technologies that can facilitate the management of information, knowledge and production decisions. The majority of intelligent systems in the petroleum sector have been applied for control and monitoring of crude upgrading processes (1). These applications improved plant operations through better control and scheduling of production, and by reducing work disruptions. The benefits accrued included reduced energy consumption, improved product recovery, reduced variation in throughput capacity, increased throughput, and improved plant optimization. The following references summarize selected applications of intelligent systems in oil production and exploration:Pumparound controls (2)Smart oil recovery (3)Scheduling/planning of production (4)UNIK-CPS (5)Slurry minder (6)AcidMan (7)Gas dehydration (8) Project Rational and Implementation In order to take advantage of Artificial Intelligence technologies in Saskatchewan's heavy oil industry, a project was undertaken to identify and demonstrate the potential for artificial intelligence in Saskatchewan oil fields. This project was conducted under a SPTRC program developed to assist the Saskatchewan petroleum industry in identifying opportunities for implementation of advanced technologies, and was cofunded by the Industry Group of CANMET, Natural Resources Canada plus Husky Energy and Petrovera. The overall objectives of this project were to assist Saskatchewan petroleum producers in:
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 ».