Analytical Model for Multi-Fractured Horizontal Wells in Tight Sand Reservoir with Threshold Pressure Gradient
Notice bibliographique
Résumé
Abstract Multi-stage fracturing is currently the most effective method to exploit tight sand reservoirs. Various analytical models have been proposed to fast and accurately investigate post-fracturing pressure- and rate-transient behaviors, and hence, estimate key parameters that affect well performance. However, these models mainly consider 2D flow, neglecting fluids flow from upper/lower reservoir when vertical fractures partially penetrate the reservoir. Although for linear flow model, Olarewaju et al. (1989) and Azari et al. (1990, 1991) have studied the effects of fracture height, they merely used a skin factor. Moreover, reservoir heterogeneity is seldom included. This paper presents an analytical model for multi-stage fractured horizontal wells (MFHWs) in tight sand reservoirs, accounting for upper/lower reservoir contributions, reservoir heterogeneity and threshold-pressure gradient (TPG). The model is extended from "five flow region" model and subdivides the reservoir into seven parts including two upper/lower flow regions, two outer flow regions, two inner flow regions and hydraulic fracture flow region. Reservoir heterogeneity along the horizontal wellbore is considered, thus, the fracture distribution can be various, and fracture pattern optimization strategies are documented. Fracture interference is simulated by locating a no-flow boundary between two adjacent fractures. The exact locations of no-flow boundaries are determined based on boundary's pressure which is a function of time and space. Thus, the no-flow boundary has minimum pressure difference between its two sides during the well production, making the no-flow assumption reasonable. The experimentally observed TPG and pressure drop within the horizontal wellbore are included. Modeling results are compared with those from well-testing software KAPPA Ecrin, obtaining a good match in most flow regimes. Specifically, the effects of upper/lower reservoir contributions and TPG are studied under constant-rate and constant-pressure conditions respectively. Log-log dimensionless pressure, pressure-derivative and production type curves are generated. Results suggest that fracture penetration ratio dominates the early-middle time pressure response. The start time of boundary-dominate flow are significantly influenced by penetration ratio. The larger the penetration ratio is, the earlier boundary-dominate flow regime will arrive. As for production response, with penetration ratio increases, the early dimensionless rate becomes larger, indicating higher flow rate, however, the late-time (tD>10) production becomes smaller, that is, the production declines quicker when penetration ratio is large. When TPG is considered, differences in both pressure and production response mainly appear in middle-late time. Greater TPG results in higher pressure drop and accelerates production decline. But this influence is marginal when TPG is small (TPG<0.4psi/ft). Effects of other relative parameters, such as formation permeability, heterogeneity, fracture length, conductivity, and wellbore storage are systematically investigated. Besides, field data are analyzed and compared graphically, using type curve matching, and reliable results are obtained. Low CPU demands and minimal data requirement of this model enable the operators to predict well testing results under different fracture patterns in a simple but effective way.
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 | 0,000 | 0,001 |
| 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 | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».