Evaluation of Continuous Fluid Injection and WAG Techniques for Enhanced Oil Recovery in a Tight Oil Reservoir in Alberta, Canada
Notice bibliographique
Résumé
Abstract Multi-fractured horizontal wells (MFHWs) completed in low-permeability (‘tight’) oil reservoirs recover only a small fraction of the oil in place using the primary recovery scheme. The main objective of this study is to investigate various continuous injection techniques for enhanced oil recovery (EOR), including water and CO2 flooding schemes, as well as water-alternating-gas (WAG) injection, to increase production from a MFHW completed in a tight oil reservoir in Alberta, Canada. This study employs numerical compositional simulation using two model types: a single porosity (SP) model with an enhanced fracture region (EFR), and a local dual-permeability, dual-porosity (L-DP-DK) model with a limited enhanced fracture region (LEFR). The SP and L-DP-DK models incorporate laboratory-derived rock and fluid data and are utilized to history-match production data from a MFHW operated under primary depletion. Multiple history-matched models are obtained to account for variability in the core- measured matrix permeability (ranging from 30 to 300 μd). The calibrated models are then utilized to predict incremental oil recovery using continuous injection and WAG EOR schemes over a 30-year period. Finally, a sensitivity analysis is conducted to study the impact of matrix permeability, fracture half-length, and fracture geometry (i.e., tip-to-tip and parallel overlapping fractures) on the simulated incremental oil recovery. The results demonstrate that the L-DP-DK model predicts a greater oil recovery (79% higher on average) than the SP model. This increase is attributed to an improved mixing and extraction process predicted by the L-DP-DK model due to more effective communication between the fracture network and the matrix. Simulation results using the L-DP-DK reveal that the CO2-gas flooding scheme has a higher recovery than WAG and water flooding techniques. CO2-gas flooding provides the greatest incremental recovery factor (i.e., 53%) when the history-matched model includes the largest permeability (300 μd) and the smallest fracture half-length (150 ft). In addition, the L-DP-DK model predicts 24% incremental recovery using the matched model with the smallest matrix permeability (30 μd) and the largest fracture half-length (500 ft). Gas flooding provides a better recovery because the oil viscosity and surface tension are two and ten times lower, respectively, than those calculated from the WAG. Moreover, the water cycle of the WAG scheme incurs blockage, leading to a quicker gas breakthrough for up to four months, and lower sweep efficiency. Finally, the sensitivity analysis reveals that the selected completion method and assumed fracture pattern significantly influence the recovery factor, with the staggered zipper completion (parallel, overlapping fractures) providing 140% greater oil recovery compared to a tip-to-tip geometry. This study provides practical insights into the impact of model uncertainty and completion methods on the design and performance of various continuous injection and WAG EOR schemes in tight oil reservoirs. Wherever possible, customized laboratory data, such as relative permeability collected for low-permeability rock samples, have been utilized.
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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».