Reservoir Characterization and Flow Simulation of a Low-Permeability Gas Reservoir: An Integrated Approach for Modelling the Tommy Lakes Gas Field
Notice bibliographique
Résumé
Summary The Tommy Lakes field is located in northeastern British Columbia, Canada, and is one of the largest Middle Triassic gas pools within the western Canada sedimentary basin (WCSB). The major gas-production formation (Halfway/Doig reservoirs) at the Tommy Lakes field corresponds to shoreface sands with permeabilities ranging between 0.1 and 3 md, and porosities of 3 to12%. For the purpose of production optimization and field development, a full-field reservoir model was developed with the integration of advanced reservoir characterization, hydraulic-fracture modelling, and history-matching techniques. This study presents an integrated workflow for modelling the low-permeability Doig gas reservoir. A stochastic geostatistical reservoir model was developed on the basis of concepts emanating from an outcrop analogue analyzed with terrestrial light detection and ranging (LiDAR) technology and 60 wells that represent the fundamental rock characteristics, structure, facies? proportions, and petrophysical properties of the Doig anomalously thick sandstone bodies (ATSBs). Structural tops were interpreted from well logs and permeability/porosity relationships established from quantitative log analysis and core/log calibration. Facies were identified in cored intervals and were further grouped into four lithofacies. An artificial neural network (ANN) was used for training the logs of key wells [gamma ray (GR), neutron porosity (NPHI), and bulk density (RHOB)] and populating the facies distribution of uncored wells. Facies-based log-derived porosity, permeability, shale volume, and water saturation were assigned to gridblocks using sequential Gaussian simulation (SGS). Finally, the Monte Carlo simulation approach was used to rank the key variables affecting original gas in place (OGIP) in the uncertainty and optimization process. Flow-based techniques were used for upscaling reservoir properties into the coarse simulation grid. The full-field simulation model was calibrated with buildup data and hydraulic-fracture modelling of single wells. Production of the Doig channel from commingled wells was allocated systematically in order to achieve a good match of the gas-production history and bottomhole pressures. Sensitivity analysis of fracture half-length and its impact on ultimate gas recovery was investigated. This concluded with an integrated development strategy. It is concluded that integration of multiple domains leads to a valid full-field reservoir model, which is critical in developing an integrated strategy, predicting reservoir performance, and optimizing gas production.
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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 | 0,001 | 0,000 |
| 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 | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| 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 ».