Prescriptive Analytics of a Surface Safety Valve at the Edge
Notice bibliographique
Résumé
Abstract A Surface Safety Valve (SSV) on wellheads and flow lines is the last line of defense to protect personnel, environment, and assets from potential catastrophic damages. With the rising demand to reduce greenhouse gases (GHG), spills, and limit operational footprints, digital transformation combined with electrification and smart algorithms are critical steps towards achieving this goal. This paper presents the recent development of a surface safety valve (SSV) controlled by an IOT-based smart emergency shutdown (ESD) system that integrates, at the wellhead, an electro-hydraulic power unit with control elements, sensors, edge computer/controller, and remote connectivity in a hybrid edge-cloud platform. Real-time data from position, pressure, and temperature sensors digitally transform the SSV and are used to feed the diagnostic algorithms. In addition to triggering an ESD event, these algorithms monitor the condition of the SSV and control circuit. This is conducted through coupling the real-time processed data with physics-based models to evaluate the SSV system health. Evaluation of the SSV and smart ESD system performance was conducted using a laboratory test unit and the major components were field tested. Sensors and control elements data was processed into three primary sets of key performance indicators (KPIs): valve health, hydraulic circuit and actuation health, and hydraulic leakage detection. The effect of varying the process line pressure on the valve signature was evaluated and compared to the available physics-based models, which showed close correlation. Moreover, coupling of the experimental data with the physics-based models was able to solve for the individual thrust load components including valve gate drag, stem force, friction, and spring pre-load from the total measured pressure. The response time during SSV closing or partial stroking was evaluated at incremental time steps to identify the source of potential malfunction in the control circuit and SSV. An emulation of hydraulic leakage revealed distinctive pressure signatures between the low- and high-pressure circuits, thus identifying the location of the leakage in the hydraulic circuit. This allows locating the exact source of malfunction in the SSV, while running prescriptive analytics to suggest component specific corrective actions. Most of the published SSV condition monitoring efforts focus on the process valve health with less attention to the hydraulic circuit, which is a critical element of the overall SSV reliability and performance. The presented smart ESD system bridges this gap by digitally transforming the SSV to provide prescriptive analytics not only to process valve components, but also the hydraulic circuit and actuator components.
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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 ».