Development and Implementation of an Automated ESP Failure Database and Reliability Analysis Platform
Notice bibliographique
Résumé
Abstract Identifying and prioritizing reliability improvement opportunities for ESPs, requires proper consideration of hundreds of parameters that include equipment characteristics, operational conditions, and root cause analyses results (Brookbank, E. B., 1997). One of the main challenges is that this data typically resides in a variety of commercial software products used internally by ConocoPhillips Canada as well as various other repositories such as spreadsheets, PDFs and numerous other internal databases and usually needs to be manually integrated for analysis. A second challenge is how to easily and consistently data mine such an extensive dataset. This paper presents the approach taken, hurdles faced, and results obtained to effectively address both challenges above. First, a failure database was designed to automatically capture time continuous data flows from various data streams, many of them flowing from commercial software tools but also some via email. To address the second challenge, an advanced visualizations and data analytics layer was developed to mine the database, in order to estimate various reliability and optimization metrics, uncover trends and generate forecasts instantaneously. Our solution was to create a unique cradle to grave ESP tracking and visualization integrated system, supporting the complete ESP reliability engineering workflow. It includes vendor to operator ESP equipment data transfer, database and nomenclature structure, operational data capture during the life of the ESP and RCA results data capturing via a 3rdparty low-code application development platform (LCADP). A visualization layer for data analytics and reliability metrics was seamlessly integrated through the use of a commercial software analytics and visualization platform (AVP). Results to date are very encouraging, both in terms of efficiency gains and quality of analysis and results. Consistent use of reliability metrics when used by different members of the production team have been achieved. Lessons learned during the development and specific examples on how the system is being used are presented, including AVP based trend visualization and failure forecast estimations. Key examples of the value captured with this Failure Database and Visualization Platform are also presented, including improved data quality, increased analytical capabilities and enhanced understanding of reliability improving options. The overall net benefit being optimized ESP life cycle costs. This development has the potential to be easily extended to other downhole production equipment such as fiber optic strings, liners, flow control devices, steam splitters and other artificial lift methods utilized in SAGD such as progressing cavity pumps.
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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 ».