A Study of the Gravity Assisted Tertiary Gas Injection Processes
Notice bibliographique
Résumé
Abstract Gravity assisted tertiary gas injection processes can produce a large amount of incremental tertiary oil from water drive oil reservoirs. These processes include the Double Displacement Process (DDP) and the Second Contact Water Displacement (SCWD) process. A transparent sandpack micromodel was developed to conduct a pore-level observation to investigate the microscopic mechanisms of the DDP and the SCWD processes. Observation of the two processes confirmed that oil films play a very important role in achieving high recovery efficiencies in the DDP. In the SCWD process, trapped gas reduces the possibility of residual oil being trapped in the centre of the pores in the second water flood. Moreover, reservoir simulations at reservoir scale were performed to investigate the macroscopic level mechanisms of the two processes. The results have shown that both processes are efficient methods to recover waterflood residual oil. Introduction A waterflood can only recover 40% - 60% of the IOIP in conventional oil reservoirs. However, it has been shown, in the laboratory, that nearly 100% of the IOIP can be recovered by tertiary gas injection in the presence of connate water(1). Recoveries of 85% to 95% of the OOIP have been reported from field tests(2 –4). This tertiary recovery method involving the up-dip injection of gas into steeply dipping, high permeability, strongly water-wet, light oil reservoirs to recover the residual oil is called the gravity assisted tertiary gas injection process. It is also known as the Double Displacement Process (DDP) because it involves the use of gas to displace the oil remaining after a waterflood(2). The high recovery efficiency made the DDP such an attractive process that numerous laboratory studies(5 –13) of the DDP have been conducted in different media to investigate the mechanisms of the process. Kantzas et al.(5, 6) showed that gravity drainage played a very important role in this process. They suggested that reservoir wettability and spreading coefficient had a great impact on the gravity assisted tertiary gas injection process. A strongly water-wet porous medium and a positive spreading coefficient were preferable in this process, and the process efficiency was dependent on the spreading phenomenon. Oren et al.(8) studied the effect of the spreading coefficient on oil recovery using a network model. Their experimental results showed that oil recovery was significantly higher for positive spreading systems than it was for negative systems. Vizika et al.(14) and Mani and Mohanty(15) confirmed these results by conducting gas gravity drainage experiments in a sandpack and a network model. The incremental oil recovered by the process consists of two parts. The first part is the bypassed oil, which exists as a continuous oil phase in the regions of the reservoir unswept by water due to reservoir heterogeneity or well placement. The second part is the residual oil existing at the microscopic scale as isolated oil blobs in the water swept regions of the porous medium due to the capillary and surface forces. The bypassed oil is recovered because gas injection improves the sweep efficiency. The trapped oil is recovered by oil film flow.
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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 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 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 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 ».