A New Physical Dispersion Model for Miscible Displacement
Notice bibliographique
Résumé
Abstract Molecular diffusion and mechanical dispersion are the main mechanisms responsible for gas-oil mixing that occurs in a miscible flood process. Most of the conventional reservoir simulators do not account for the these physical mechanisms and presume it to be compensated by the numerical dispersion arising out of the finite-difference scheme with single-point upstream weighting of mobilities for the sizes of gridblocks normally used in field-scale simulations. Numerical dispersion is artificial and non-physical and the assumption that it can compensate for physical dispersion can lead to erroneous results. The multipoint flux approximation (MPFA) scheme developed in recent years provides an improved method for modelling tensorial permeabilities in non-uniform and skewed grids that are often required for proper representation of the reservoir geometry. The physical dispersion coefficient in the dispersive flux is tensorial in nature and amenable to a treatment similar to that of the permeability tensor in the convective flux. We have applied a multipoint control-volume scheme to the dispersive flux in a compositional simulator with a two-point upstream weighting and total variation diminishing (TVD) implementation to minimize the effect of front smearing caused by numerical dispersion. In this paper we present salient features of the proposed formulation and the basic results for miscible displacements in a linear model, and in a quarter of a five-spot pattern with non-orthogonal grids. Introduction Dispersive mixing plays an important role in the performance of a miscible displacement process. It determines how much of the solvent will mix with the in-situ oil to promote miscibility under favorable conditions. Dispersion is the process of distributing or spreading concentration profiles due to mechanisms in which the flux is proportional to the concentration gradient. Diffusion is a special case of dispersion when the velocity of the fluid is zero. The diffusion process was first recognized by Fick. Perkins and Johnston1 suggested that dispersion in porous media is Fickian in nature and the dispersive flux can be obtained by reducing the crosssectional area by multiplying it with porosity. Two basic elements of dispersive mixing are molecular diffusion and mechanical dispersion2. For mechanical dispersion to occur variation in convective velocity field is required, which is created by the tortuous flow paths of the porous network and/or by imposed changes in the strength of sources or sinks. The inhomogeneity in porous medium promotes mechanical dispersion. Molecular diffusion, however, takes place solely due to concentration gradient, with or without the presence of motion. A number of crossflow mechanisms3,4 are responsible for mass transfer in a gas displacement process, viz., diffusion, dispersive mixing, capillary pumping, interfacial tension effects and relative permeability modification. The present paper deals primarily with the first two factors i.e. molecular diffusion and mechanical dispersion. Young5 used the convection-diffusion equation and a one-dimensional grid to model the multi-contact miscible process by treating the dispersion coefficient as a function of the viscosity gradient. The formulation used centered differences for evaluating convective and dispersive fluxes.
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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 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».