Vaca Muerta: Improved Fracture Width Distribution and Classification of Natural Fracture Widths Based on Outcrops, Cores, and Microresistivity Images Data
Notice bibliographique
Résumé
Summary Natural fractures in Vaca Muerta are very complex, such that their fracture width distributions cannot be analyzed simply by considering normal, log-normal, or log-log distributions. Natural fractures are commonly classified as macrofractures or microfractures; however, no consistent fracture width is attached to those fractures. In this study, two new approaches are proposed; an improved fracture width distribution and a classification for natural fractures that encompasses all physical widths found in petroleum reservoirs. The method developed in this study first evaluates the distribution of natural fracture widths from outcrops, cores, and microresistivity images of Vaca Muerta shale. An improved fracture width distribution is established through a variable shape distribution (VSD). The model provides a good fit, even if the shape of the distribution deviates from generally accepted distributions. This improves the accuracy of fracture width and intensity prediction, which is useful in generating synthetic production logging tools (PLTs) to estimate productivity from fractured intervals. Subsequently, a consistent classification for natural fractures is introduced to cover all fracture widths found in petroleum reservoirs. Results indicate that fracture widths in Vaca Muerta shale range between 0.0003 mm and 7 mm for outcrops, 0.0003 mm and 2 mm for cores, and 0.01 mm and 2 mm for microresistivity images. The VSD model provides a good fit of fracture widths from the three sources, without truncating any of the data. Truncation of data is usually required when using generally accepted distributions. With this improved distribution, size pattern extrapolation can be performed with greater accuracy. The physical widths can also be translated into hydraulic apertures to generate theoretical PLT. This is useful for estimating relative petroleum production potential from each fractured interval and for identifying future refracturing zones. Additionally, the study gives origin to a consistent classification of fracture widths that has application in Vaca Muerta and other oil and gas reservoirs. Five subclasses are introduced, which are megafractures (> 10 mm), macrofractures (1–10 mm), mesofractures (0.1–1 mm), microfractures (0.01–0.1 mm), and nanofractures (<0.01 mm). A careful review of the literature indicates that there is ambivalence as it is hard to find a clear and precise terminology that encompasses the entire range of fracture widths. The proposed classification eliminates that difficulty. In this paper, for the first time, a consistent fracture width classification is developed that encompasses the whole spectrum of widths found in petroleum reservoirs. It has wide application in Vaca Muerta, where widths, derived from outcrops, cores, and microresistivity image data are matched with a VSD model. Furthermore, the proposed classification can be used in other oil and gas reservoirs, thus eliminating the fracture width ambivalence found many times in the geoscience and petroleum engineering literature.
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 distillée sur la base complète
Imitation des enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».