Extreme Limited Entry Design Improves Distribution Efficiency in Plug-n-Perf Completions: Insights from Fiber-Optic Diagnostics
Notice bibliographique
Résumé
Abstract Limited entry (LE) plug ‘n’ perf (PnP) fracture designs were pioneered in the early 1960s as a cost-effective technique to stimulate multiple pay zones with varying stress regimes (Murphy & Juch, 1960). Conventional completion techniques would involve blanket perforating the entire interval with 4 shots per foot. The technique was revolutionary in that it recommended "limiting" the number of perforations to distribute fracture stimulation fluids into multiple intervals with differing stress regimes. However, diagnostics have shown that LE treatment distribution during the slurry phase is uneven and is highly impacted by several key parameters that may change significantly during treatment. Several papers have been published on the inefficiencies associated with LE design and what can be done to overcome them (Ugueto, Huckabee, Molenaar, Wyker, & Somanchi, 2016) (Somanchi, O' Brien, Huckabee, & Ugueto, 2016). Shell Canada Ltd. recently tested eXtreme Limited Entry (XLE) designs to determine if additional pressure drop across the perforations would improve treatment distribution. Stages were alternated with differential perforation friction (∆P) pressures of 2,000, 2,500, and 3,000 psi to determine if there was a threshhold ∆P that would result in a more optimal treatment distribution. However, due to wellhead pressure limitations, actual ∆Ps were below the design values. There were no placement issues associated with fewer perforations and higher treatment pressures. The trial well was completed with thirteen 3-cluster stages. All clusters were evenly spaced at 50 meters and fracture stimulated with a slickwater system with 31 tons/cluster (93T/stage). The fracture stimulation was monitored using an externally clamped fiber-optic (FO) cable. Treatment distribution and production were quantified using Distributed Acoustic Sensing (DAS) (Molenaar & Cox, 2013). Post-job analysis indicates a 40% improvement in distribution compared to previously stimulated 3-cluster standard LE completions. With the XLE design, 100% of clusters received some proppant. There is a 33% increase in cluster activity at IP90 from the XLE design compared to a previously completed 3-cluster conventional LE well. Improvement in distribution is minimal beyond ∆P of 1200 psi during the pad phase. However, this threshold could be rock-specific and needs to be validated with trials in different play types. Data also suggests that treatment pressure should be maintained at a maximum throughout the pad and slurry placement, within equipment and wellhead limitations. During pad, this is important to ensure breakdown and fracture extension. In the slurry phase, maxing out pressure helps to maintain ∆P across eroding perforations. In some plays, insufficient ∆P may prevent all clusters from breaking down. In Groundbirch, typically all clusters breakdown and take fluid from the start but screenout as soon as sand hits. Howeever, slurry rate is typically not increased to compensate for the loss in ∆P due to an increase in perf diameter. These factors are largely responsible for the heel-toe bias in LE designs which results in under-treatment of toe clusers. (Ugueto, Huckabee, Molenaar, Wyker, & Somanchi, 2016)
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 | 0,001 | 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 | 0,000 | 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 ».