Cold Heavy Oil Reservoir Characterization by Time-lapse Seismic Inversion, a Case Study
Notice bibliographique
Résumé
Abstract Cold heavy oil production with Sand (CHOPS) is one of the major techniques which is applied in heavy oil production fields, especially in Canada. In this technique, high viscosity heavy oil is extracted with sand by using cavity pumps. In addition to causing fluid changes in the reservoir during the period of production, the CHOPS technique causes changes in the mass of the reservoir rock during the production time due to the simultaneous extraction of sand and oil. Therefore, it is important to model the reservoir changes in CHOPS fields in order to determine how to increase the recovery of the heavy oil. Time-lapse seismic analysis has proven to be one of the best methods which is capable of monitoring production-related changes beyond the wells. Time-lapse seismic analysis involves the use of repeated seismic surveys which are acquired in the same area. By analyzing the differences between these repeated surveys we are able to infer changes due to production. To start, rock physics modeling was done on the available well log data from wells in the area to model how fluid substitution in the reservoir would change elastic properties such as P-wave velocity, S-wave velocity and density within the reservoir. This modeling can help us understand how elastic properties extracted from time-lapse seismic data can be interpreted. Rock physics modeling was done by using the Gassmann equation and applying the Batzle and Wang relationships for fluids. Next, 4D calibration steps were applied to the time-lapse seismic data to reduce non-reservoir related anomalies and allow us to focus only on production-related changes in the reservoir. Model-based post-stack and pre-stack seismic inversion was then applied to the time-lapse seismic data to extract the elastic properties at both horizontal and vertical scales, over one of the CHOPS fields in Saskatchewan, Canada. Different elastic volumes such as time-lapse acoustic impedance, shear impedance and density were output from the seismic inversion process. Other time-lapse seismic attributes such as Lambda-Rho (P-impedance squared minus two times S-impedance squared) and Mu-Rho (S-impedance squared) were also computed after the performing time-lapse seismic inversion. The changes observed from the time-lapse seismic inversion and other time-lapse attributes helped us to recognize high production zones and to delineate the areas which could be targeted during the next recovery steps in the reservoir production. Combining rock-physics modeling and time-lapse seismic analysis provided us with a useful tool to monitor the production processes in the reservoir between wells. The seismic results are in good agreement with observed production changes and reservoir simulation results.
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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 | 0,000 | 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 | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 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 ».