Interventions in Challenging Low Pressure Horizontal Producers with Known Casing Deformation: A Case Study Comparing Conveyance Methods for Successful Installation of Toe Gas Lift Systems
Notice bibliographique
Résumé
Abstract This paper presents a case study of intervention/workover operations from a nine-well pad in the Montney formation, in British Columbia, Canada. The operational objective was to land artificial lift near the toe of each wellbore. Several conveyance methods were evaluated, and jointed pipe was ultimately selected. This paper highlights key learnings gathered throughout the operation. Outputs from real-time Electronic Drilling Recorder (EDR) data are included and contrasted with other intervention methods. Torque and drag simulations showed the desired depth was achievable using offset friction factors that were calibrated using coiled tubing conveyance during initial post-frac plug drillout operations. Jointed pipe intervention was selected to reduce uncertainty and understand limitations of installing artificial lift at depths near the toe of a low-pressure wellbore with known casing deformation. A telescopic-double workover rig package was utilized throughout operations along with rig-assisted snubbing on early wells where surface pressure existed. A confirmation run was made using a mock assembly to gauge reach capabilities prior to running the final assembly. Overall, the program was a success with seven of nine wells achieving 85% lateral coverage. Major scope changes were encountered on Wells 1 and 2, requiring sixteen and nine days to complete, respectively, and a stuck pipe event on Well 6 will be discussed. Key challenges include sub-optimal bottomhole assembly selection, poor circulating efficiencies, variable subnormal bottomhole pressure, and casing deformation. Real-time hook load data correlated to the Torque and Drag model. However, observed torque was significantly higher than presented by the model. This is suspected to have been caused by micro-tortuosity and localized doglegs. Despite challenges on several wells, key optimizations were quickly implemented to accelerate the learning curve towards ultimate positive economic results. Learning how to select the right conveyance method based off changing well conditions is a key driver in minimizing risk, optimizing profitability, and maximizing the chance of success. Learnings and results from this nine-well program will provide a framework for success in future jointed pipe interventions.
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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 | 0,001 | 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 ».