Scaling A Water Coning Control Installation in Bottom Water Drive Reservoir by Inspectional Analysis
Notice bibliographique
Résumé
Abstract This paper presents an integrated workflow dedicated to the interpretation of 4D seismic data to monitor the steam chamber growth during the Steam-Assisted Gravity Drainage recovery process (SAGD). Superimposed to the reservoir heterogeneities of geological origin, many factors interact during thermal production of heavy oil and bitumen reservoirs, which complicates the interpretation of 4D seismic data: changes in oil viscosity, fluid saturations, pore pressure, etc. The workflow is based on the generation of a geological model inspired from a real field case of the McMurray Fm in the Athabasca region (Canada). The approach consists of three steps: 1/ the construction of an initial static model, 2/ the simulation of the thermal production of heavy oil with two coupled fluid-flow and geomechanical models, 3/ the production of synthetic seismic maps at different steps of steam injection. The distribution of geological facies is simulated on a very fine grid using a geostatistical approach which honours all available well data. The reservoir, geomechanical and elastic properties are characterized from logs and literature at an initial stage before the start of production. Production scenarios are run to obtain pore pressure, temperature, steam and oil saturations on a detailed reservoir grid around a well pair at several steps of production. Direct coupling with a geomechanical model leads to volumetric strain and mean effective stress maps as additional properties. These physical parameters are used to compute new seismic velocities and density for each step of production according to Hertz and Gassmann formulas. Reflectivity is then computed, and a new synthetic seismic image of the reservoir is generated for each step of production. The impacts of heterogeneities, production conditions and reservoir properties are evaluated for several simulation scenarios from the beginning of steam injection to three years of production. Results show that short-term seismic monitoring can help in anticipating early changes in steam injection strategy. In return, long-term periods allow monitoring the behaviour of the steam chamber laterally and in the upper part of the reservoir. This study demonstrates the added value of 4D seismic data in the context of steam-assisted heavy oil production. Introduction The performance of heavy-oil production by Steam-Assisted Gravity Drainage process (SAGD) is affected by reservoir heterogeneities. However, as many factors interact during thermal production such as changes in oil viscosity, fluid saturations, pore pressure and stresses, the interpretation of 4D seismic data in terms of steam chamber geometry is not direct. Pressure and temperature variations during SAGD operations induce stress changes in the reservoir and in the surrounding media. These modifications of the stress state may imply deformations which can in turn have an impact on reservoir production. These changes also have an influence on the wave propagation into rocks and fluids and may consequently produce differences on seismic velocities and on the travel time. The objectives of this work are 1/ to evaluate the impact of reservoir heterogeneities on the steam chamber growth and 2/ to improve the interpretation of 4D seismic data in steam-assisted.
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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 | 0,001 | 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 | 0,000 |
| 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 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 ».