One Stage Forward or Two Stages Back: What Are We Treating? Identification of Internal Casing Erosion during Hydraulic Fracturing—A Montney Case Study Using Ultrasonic and Fiber-Optic Diagnostics
Notice bibliographique
Résumé
Summary Plug-and-perforation (plug-and-perf) multistage hydraulic fracturing completions in unconventional reservoirs rely on complete hydraulic isolation from the previous stage to ensure effective treatment of the active stage. Failure to isolate stages can be a result of partially set plugs, plugs set in wellbore debris or deformed casing, unqualified pressure/temperature rating of plugs, and so on. This paper presents a case study with field examples in which unexpected casing erosion occurred at the setting depths of the dissolvable fracturing (frac) plugs during hydraulic fracturing and subsequently resulted in loss of interstage isolation. A 12-well, four-layer, cube pilot was designed with permanent fiber-optic cable to collect distributed acoustic sensing (DAS), distributed temperature sensing (DTS), and distributed strain sensing (DSS) data as well as downhole pressure gauges for development insight and future completion optimization. The cable was mapped, and oriented perforation techniques placed entry holes opposite the fiber along the wellbore, and no loss of communication was observed during perforating operations. However, fiber-optic signal was lost during hydraulic fracturing operations on one or more stages in all four instrumented horizontal wells. Real-time DAS/DTS analysis indicated the fiber breaks were consistently occurring below the lowermost perforation cluster in the stage, at or very near the frac plug setting depth. Step-down tests were also performed and showed significantly enlarged effective treating area. Based on this observation, post-frac downhole imaging tools were deployed to investigate potential casing and perforation erosion. Downhole imaging data clearly showed the casing was severely eroded at several locations. Additional interrogation of the damage with respect to plug design components indicated that damage always occurred near the plug sealing element. By integrating the analysis of DAS/DTS, step-down tests, and ultrasonic imaging, it was determined that the frac plug bypass was creating a loss of casing integrity at the plug set location. Casing integrity loss resulted in multiple fiber-optic cable breaks and lowered the ability to evenly distribute slurry into treatment clusters. Fiber-optic data analysis showed that 50% of the larger outer diameter (OD) dissolvable frac plugs had bypass compared to 100% bypass for the smaller OD high-expansion, dissolvable plugs. To establish key patterns and identify critical variables that influence stimulation effectiveness, it is important to obtain several different diagnostic data sets and perform an integrated evaluation using all available information. This study also reinforces the need for operators and manufacturers to work together to design and qualify frac plugs against realistic downhole conditions, particularly in areas with potential casing deformation issues. Industry innovation is required to enable fracturing operations to continue through deformed casing. This includes advancing equipment, tools, and techniques for plug-and-perf and other multistage completion methods.
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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 | 0,006 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,004 |
| Communication savante | 0,005 | 0,005 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,004 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 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 ».