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Enregistrement W2886091558 · doi:10.2118/184834-pa

Extreme Limited-Entry Design Improves Distribution Efficiency in Plug-and-Perforate Completions: Insights From Fiber-Optic Diagnostics

2018· article· en· W2886091558 sur OpenAlex

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Notice bibliographique

RevueSPE Drilling & Completion · 2018
Typearticle
Langueen
DomaineEngineering
ThématiqueHydraulic Fracturing and Reservoir Analysis
Établissements canadiensShell (Canada)
Organismes subventionnairesnon disponible
Mots-clésWellheadSpark plugFracture (geology)Well stimulationPerforationEngineeringGeologyPetroleum engineeringGeotechnical engineeringMechanical engineering

Résumé

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Summary Limited-entry (LE) plug-and-perforate (PnP) fracture designs were pioneered in the early 1960s as a cost-effective technique to stimulate multiple pay zones with varying stress regimes (Murphy and Juch 1960). Conventional completion techniques involved blanket perforating the entire interval at a certain number of shots per foot (spf). The LE 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 affected 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 performed to overcome them (Ugueto et al. 2016; Somanchi et al. 2016). Shell Canada Limited 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 (ΔP) pressures of 2,000, 2,500, and 3,000 psi to determine if there was a threshold ΔP that would result in a more-optimal treatment distribution. However, because of wellhead-pressure limitations, actual ΔPs 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 three-cluster stages. All clusters were spaced evenly at 50 m and fracture-stimulated with a slickwater system with 31 tons/cluster (93 tons/stage). The fracture stimulation was monitored with an externally clamped fiber-optic (FO) cable. Treatment distribution and production were quantified by using distributed acoustic sensing (DAS) (Molenaar and Cox 2013). Post-job analysis indicates a 40% improvement in distribution compared with previously stimulated three-cluster standard LE completions. With the XLE design, 100% of the clusters received some proppant. There is a 33% increase in cluster activity at IP90 (initial production on the 90th day) from the XLE design compared with a previously completed three-cluster conventional LE well. Improvement in distribution is minimal beyond ΔP of 1,200 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 suggest that treatment pressure should be maintained at a maximum throughout the pad and slurry placements, within equipment and wellhead limitations. During the pad, this is important to ensure breakdown and fracture extension. In the slurry phase, maximizing out pressure helps to maintain ΔP across eroding perforations. In some plays, insufficient ΔP may prevent all clusters from breaking down. In Groundbirch, typically all clusters break down and take fluid from the start but screen out as soon as sand hits. Typically, slurry rate was not increased to compensate for the loss in ΔP associated with an increase in perforation diameter. These factors are mainly responsible for the heel-vs.-toe bias in LE designs, which results in undertreatment of toe clusters (Ugueto et al. 2016).

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Prédiction distillée sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: Simulation ou modélisation
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,269
Score d'incertitude au seuil0,972

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,023
Tête enseignante GPT0,219
Écart entre enseignants0,196 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle