Understanding Hydraulic Fracture Geometry and Interactions in the Horn River Basin through DFN and Numerical Modeling
Notice bibliographique
Résumé
Abstract In the Horn River Basin, evidence points to one of the key elements of a successful well completion being the effective connection of the hydraulic fracture into the natural fracture system, allowing the well to connect to a significantly enhanced drainage volume. However great uncertainty exists with respect to the nature of the natural fracture system and importantly how the hydraulic fracture interacts with it. To provide a framework to address these uncertainties, an approach has been developed within a Discrete Fracture Network (DFN) code. The DFN approach provides a platform to construct realistic fracture models of stochastically generated fracture elements constrained and conditioned by well and surface data. Simulation of hydraulic frac development through these DFN models using a rule based approach allows the rapid modelling of the interaction between the hydraulic frac and the natural fracture system with calibration of the model being provided by the generation of a simulated micro-seismic cloud. Comparison of the simulated micro-seismic pattern to field measurements increases confidence in the DFN approach and allows the tuning of key hydraulic properties as part of the calibration process. An additional challenge at this stage of the plays development however is that many of the developments are relatively data poor and therefore the rigorous simulation of detailed models conditioned to well data is often not possible. To address this, a number of simulations were run on more generic models where key properties such as fracture length, fracture aperture and intensity were varied and their impact on the resultant micro-seismic pattern observed. This more parametric approach allows well observations to be interpreted within a better constrained framework of fracture network knowledge. These DFN based simulations were supplemented by detailed geomechanical models using a hybrid FEM-DEM code that allowed the coupled stress-flow modelling of hydraulic frac interaction and pressure evolution, enabling certain stimulation design factors to be considered as well as testing the basis for the more stochastic modelling. The benefit of these combined simulations is that a framework is developed to integrate, interpret and test all the fracture related information, allowing more guided development decisions to be made as well as identifying critical data gaps to be addressed.
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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 ».