Adding Depth: The Superiority of Volumetric Perforation Erosion Analysis for Evaluating Stimulation Performance and Optimizing Completion Designs
Notice bibliographique
Résumé
Objectives/Scope Permanent fiber optics and high-resolution acoustic imaging are two diagnostic tools commonly used to assess completion design performance in horizontal wells. Fiber optics offer real-time data at the cluster level but can be costly and prone to mechanical failure. In contrast, acoustic imaging is much more affordable but only captures temporal snapshots of data. This paper thoroughly examines how advanced perforation (perf) erosion analysis with three-dimensional (3D) measurements can produce analytical results comparable to fiber. Methods, Procedures, Process Perf tunnel volumes are calculated using measurements obtained from high-resolution acoustic imaging technology, which has been laboratory-validated against a metrology-grade laser scanner. These measurements are used to generate a novel diagnostic plot that explains perf erosion behavior in conjunction with theoretical growth cases. This plot is also used to empirically determine uneroded perf tunnel volumes for different regions around the borehole, which are subtracted from eroded volumes to calculate growth at the perf, cluster, and stage levels. The growth volumes are then used to calculate treatment uniformity with respect to inferred proppant placement for different stage configurations. Results, Observations, Conclusions Treatment uniformity values determined from volumetric perf erosion analysis are strikingly similar to those determined from permanent fiber optics using Distributed Acoustic Sensing (DAS) data collected in real-time during stimulation. In the case of a horizontal Niobrara well analyzed with both diagnostic technologies, the conclusions independently determined from both datasets yielded the same answer in terms of which stage configuration should be utilized for future field development plans. Hydraulic fracture profiles (HFPs) were also generated for stages that had all perf measurements available for analysis. When these HFPs were directly compared to those generated from permanent fiber optics for the same stages, they tended to show a tremendous amount of similarity with respect to inferred cluster-level proppant placement and treatment bias. This alignment between fundamentally different diagnostics not only demonstrates the strength and veracity of volumetric perf erosion analysis, but it also helps validate the new method of empirically determining uneroded perf tunnel volumes using 3D measurements. Novel/Additive Information Although acoustic imaging technology has been used extensively for several years, erosional analyses have typically been performed using exit hole measurements exclusively. Only recently has 3D volumetric perf erosion analysis been evaluated. It is clear from the learnings that exit hole measurements alone do not account for all the subtle nuances associated with proppant placement and erosional phenomena. These additional measurements allow perfs to be represented in 3D space, which has helped bridge the gap between different diagnostics that historically lacked alignment and left operators wondering in which dataset to place their confidence.
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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) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».