An Integrated Completion and Reservoir Modeling Methodology for Horizontal Shale Wells: A Montney Formation Example
Notice bibliographique
Résumé
Abstract The Montney Formation is one of the largest unconventional resources in North America covering from southwestern Alberta to northeast British Columbia. The Montney Formation has natural gas, natural gas liquids (NGL) and oil from conventional and unconventional reservoirs. The first multiple fractured horizontal well (MFHW) was drilled in 2005. However, since the first MFHW, different methods were proposed to optimize completion designs in the Montney Formation. Some of the optimized completions utilized the "operational effectiveness" of high-rate slick-water fracture designs while other designs utilized energized fracturing fluids. What has been missing was an integrated methodology that utilizes all available data to improve well stimulation and productivity. The objective of this paper is to present a new methodology for selecting lateral well placement, completion strategy and determination of stimulated reservoir volume (SRV) by integrating available data such as curvature data from 3D seismic, micro-seismic, geo-mechanical data, logs, fracability index, mini-frac test, step-rate test, DFIT analysis, core data, and fracture treatment design to optimize well productivity, hydrocarbon recovery and economics. The process of developing the hybrid model involved integrating the completions design with compositional reservoir simulator using a two-step process; first, the hydraulic fracture design was calibrated using only the micro-seismic data from the stages that were closest to the geophone/receiver (avoiding location bias or signal-noise ratio issues) in order to develop a reliable fracture geometry model. The calibrated fracture model was used for history matching and re-modeling of all the fracture stages in each well. Fracture geometry and dimensions for each stage were obtained from the calibrated fracture model. Secondly, the compositional reservoir simulators were built using reservoir geology, PVT data, production data and well deviations. Fracture dimensions obtained from the calibrated fracture model were then transferred into the reservoir simulator. Finally, curvature data obtained from 3D seismic was used to predict the location of secondary fissures within the well drainage area, and were then incorporated into the compositional reservoir simulator. The result from this study shows that the hybrid integrated completion and reservoir model can be used for the selection of optimum lateral placement targeting sweet spots that have secondary fissures and good fracability index to maximize production rates, hydrocarbon recovery and to improve well economics. Additionally, this study presents a new hybrid model for determining a representative stimulated reservoir volume (SRV) with discrete fracture networks that captures secondary fissures, which can then be used for production history matching and forecasting. The key features of this work that will benefit the petroleum industry are:A new methodology for building calibrated fracture model using micro-seismic survey even if the micro-seismic data is of low quality as a result of location bias or signal-noise ratio issuesExtending the stimulated reservoir volume (SRV) to include secondary fissure contributions to the overall well production and recovery.Use of a discrete fracture network with stress dependent fracture permeability in the compositional reservoir simulator to capture the effects of geomechanical changes during depletion.A comparison of well productivity and EUR derived from a planar fracture model versus discrete fracture network based reservoir models.
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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) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 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) | 0,000 | 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 ».