A Mechanistic Analysis of Viscous Fingering in Low-Tension Polymer Flooding in Heavy-Oil Reservoirs
Notice bibliographique
Résumé
Abstract Low tension polymer flooding (LTPF) can be considered as an alternative enhanced oil recovery method for some heavy oil reservoirs where thermal and miscible gas-injection methods face technical and environmental challenges. To properly tailor LTPF to a heavy oil reservoir, it should be optimized via simulation in the lab, pilot, and field scales. However, simulation normally suffers from the paucity of detailed knowledge of viscous instability or fingering effects, due to displacing a more viscous fluid by a less viscous one. This instability reduces displacement efficiency and may invalidate usual method of simulating LTPF performance based on relative permeability and capillary pressure concepts. Also, it introduces an additional scaling requirement for using results of experiments in larger scales. Thus, predicting instability nature is of particular concern, to avoid viscous fingering, or, where it is inevitable, to be capable to include it as an additional factor in modeling displacement. Major limitations of previous approaches for studying viscous fingering in immiscible displacements are that reported experiments have been conducted utilizing linear displacements schemes in media with high, single-phase permeabilities. Consequently, the questions that arise are whether previous findings can be valid in low-permeability media and using displacement schemes similar to the oil-field patterns (e.g., five-spot). In oil-field patterns, one has to deal with varying velocity profiles from injector(s) to producer(s). Because velocity is one controlling factor in viscous fingering, the effect of dispersion caused by varying velocity profiles has not been tested completely on viscous fingering in previous experimental studies. To help understand viscous fingering in LTPF in heavy oil reservoirs and to overcome the limitations of previous studies, we conducted experiments in low-permeability, one-quarter, five-spot patterns. New insights into the main driving mechanisms for viscous fingering are proposed. In summary, the mechanisms of spreading, splitting, coalescence, and microscopic crossflow drive the finger growth. In addition, the viscous fingers are readily initiated in the porous medium, but they can be damped out before traveling very far. This damping of the viscous fingers is due to the flow of the two phases in a direction transverse to the direction of bulk fluid movement (along the mean free path) as a result of dispersive processes such as stream splitting. Also, the initially- developed fingers may deteriorate over the time of displacement. This depends on the distance between the injector and producer and width of the porous medium. The presence of instabilities that look like fingers and stable displacements behind the unstable front were discovered. The results also indicate that a stable zone exists and progresses throughout the porous medium at varying velocities. This was the reason for considerable ultimate recovery observed after breakthrough where numerous pore volumes were injected. Finally, we reveal three different types of displacements that occurred: stable displacements, displacement with macroscopic viscous fingering, and displacements with both macroscopic and microscopic viscous fingering. Therefore, in LTPF, macroscopic and microscopic fingering exist and flow theory must include both to match the experimental results. Additionally, the differences between microscopic and macroscopic fingering has not been incorporated in available reservoir simulations. Hence, these two phenomena need to be modeled in simulations.
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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 | 0,000 |
| 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 | 0,001 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».