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Enregistrement W2913991294 · doi:10.2118/194334-ms

Integrating DAS, Treatment Pressure Analysis and Video-Based Perforation Imaging to Evaluate Limited Entry Treatment Effectiveness

2019· article· en· W2913991294 sur OpenAlex

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Notice bibliographique

RevueSPE Hydraulic Fracturing Technology Conference and Exhibition · 2019
Typearticle
Langueen
DomaineEngineering
ThématiqueHydraulic Fracturing and Reservoir Analysis
Établissements canadiensConocoPhillips (Canada)
Organismes subventionnairesnon disponible
Mots-clésPerforationHydraulic fracturingPetroleum engineeringFracture (geology)Computer scienceGeologyGeotechnical engineeringEngineeringMechanical engineering

Résumé

récupéré en direct d'OpenAlex

Abstract The primary objectives of perforating a lengthy cased-and-cemented wellbore section for fracture stimulation are to 1.) enable extensive communication with the reservoir and 2.) control the allocation of fluid and proppant into multiple intervals as efficiently as possible during fracturing treatments. Simultaneously treating multiple intervals reduces the number of fracturing stages required, thus reducing treatment cost. One way to control the allocation is to use limited entry perforating. Limited entry is the process of either limiting the number of perforations or reducing the size of the perforation entry-hole to achieve significant perforation friction pressure during a hydraulic fracturing treatment. Perforation friction establishes a backpressure in the wellbore that helps to allocate flow among multiple, simultaneously-treated perforation intervals/clusters that have differing fracture propagation pressures. Execution and optimization of limited entry perforating requires awareness of the factors that can affect performance. This paper presents a case study of plug-and-perf horizontal well treatments in an unconventional shale play in which various diagnostic methods were used to better understand and quantify these factors. Within the case study, three types of perforation evaluation diagnostics were implemented: 1.) injection step-down tests and pressure analysis of the fracturing treatments, 2.) video-based perforation imaging and 3.) distributed acoustic sensing (DAS). Injection step-down tests indicated that all perforations were initially accepting fluid. However, history-matched solutions of step-down tests are non-unique due to multiple variables involved in the calculations and uncertainty regarding the exact initial-perforation conditions. Surface pressure analysis of the main fracturing treatments indicated that in certain cases, several perforations were not accepting fluid and proppant (slurry) by the end of the job. The number of inactive perforations was typically equivalent to the amount contained in two clusters. Video-based imaging highlighted several trends and concepts for perforating. Zero-phase perforating toward the high side of the well was advantageous for obtaining quality images and relatively consistent perforation dimensions. A large majority of perforations showed unambiguous qualitative evidence of significant proppant entry. Even though images captured were post-stimulation, it was apparent that initial perforation dimensions were significantly smaller and gun phasing had a more significant effect than originally predicted. Evaluation of the erosion patterns on the perforations showed a positional bias where for a given frac stage, perforations in clusters nearest the heel of the well were more eroded than perforations in clusters nearest the toe of the well. Distributed acoustic sensing (DAS) analysis confirmed the conclusions of the surface pressure analysis. In the example provided, the data showed all clusters accepting fluid during the step-down test. Later in the stage, the DAS data showed two clusters not accepting fluid at different times of the stage. DAS analysis was able to confirm the timing and position of the two clusters. The DAS data also showed a positional bias, allocating more slurry volume to clusters nearest the heel of the well. However, DAS analysis also showed that changing the number of perforations in a cluster had a larger effect than the positional bias. The staggered perforation design featuring two fewer perforations in the cluster closest to the heel effectively counteracted the positional bias but resulted in diverting too much slurry volume from that cluster. The results also highlight the importance of perforator quality control in terms of perforation hole size. Treating pressure and DAS analysis indicated a particular cluster stopped taking slurry relatively early in the treatment and post-frac imaging dimensioned the hole sizes and revealed they were extremely undersized from the expected hole size. Based on the results of the case study, it was recommended to use a staggered perforation design with more gradual changes. This was verified with modeling using updated parameters which showed that the resulting changes are likely to improve slurry allocation.

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.

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 candidatesMéta-épidémiologie (sens strict)
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 enseignants0,200
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,001
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 Opus0,007
Tête enseignante GPT0,243
Écart entre enseignants0,236 · 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