Successful Field Implementation of an Integrity-Focused Digital Monitoring System for Tubular Running in a Challenging Thermal ERD Application
Notice bibliographique
Résumé
Abstract Horizontal liners in extended-reach drilling (ERD) wells can experience severe loading during running. Sometimes, downhole loads approach the limits of the tubular system and must be actively managed to ensure long-term well integrity. This paper describes a Canadian thermal operator's approach to managing installation and service performance of slotted liner and wire-wrapped screen systems in a steam-assisted gravity drainage (SAGD) application with unwrapped reach ratios approaching 13:1, and the associated evolution of liner running practices. The Operator's approach combines well-characterized liner body installation loading limits and a rigsite digital solution that leverages available measurements and a real-time torque-and-drag and tubular integrity monitoring system to inform the drilling team during running. Surface loads and rates measured by the rig are used as input to top-down torque-and-drag analysis to estimate downhole load distributions. Those downhole load estimates are then compared to the local loading limits of the liner at all depths. These local loading states (and their associated uncertainties) are integrated into a safe surface loading envelope that is displayed to the drilling team and updated in real time to support running decisions. The evolution of the Operator's running practices has provided a strong basis for confidence in protecting a critical tubular system, and over 250 liner runs have been monitored to date using the digital system. Prior to implementing the system, a conservative approach to managing downhole loads during liner running was used. The integration of a strong engineering basis for the tubular structure with top-down torque-and-drag analysis and uncertainty characterization has provided a running optimization basis and measurable indicators of tubular health that can serve as an enduring quality record and be referenced for the remainder of the well life. Forecasting of running loads and liner limits to total depth has also enabled early recognition of running challenges and opportunities for optimization. Interestingly, the edge-deployed digital system has also led to operational efficiencies during the running process. Running stages involving higher risk to tubular integrity are recognized early and treated with due care, as are opportunities for increasing the efficiency of certain parts of the running process. As the Operator considers longer-reach wells, the system also provides insights into likely running challenges and provides strong history-match datasets that provide a field-calibrated basis for predicting running and tubular integrity limits. The Operator leveraged a novel digital methodology for monitoring liner system integrity during well construction. The ongoing use of this system has allowed optimization of planning, real-time, and post-run practices, and provides a well-conditioned historical dataset for future well planning. The methodology has enabled the Operator to unify work done by drilling engineers, consultants, and the rig crew for optimal liner system integrity and running efficiency.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
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 source (Gemma direct ou Codex distillé), 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 ».