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Record W2053885904 · doi:10.2118/05-05-01

Modelling Physical Dispersion in Miscible Displacement-Part 1: Theory and the Proposed Numerical Scheme

2005· article· en· W2053885904 on OpenAlexafffund
Vijay Shrivastava, Long X. Nghiem, R.G. Moore, T. Okazawa

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2005
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsImperial Oil (Canada)University of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaHokkaido University
KeywordsDispersion (optics)MechanicsDisplacement (psychology)Porous mediumGeologyPhysicsPorosityGeotechnical engineeringOptics

Abstract

fetched live from OpenAlex

Abstract Physical dispersion, comprising molecular diffusion and mechanical dispersion, is one of the primary fluid mixing mechanisms in reservoir processes dominated by compositional change. Its effect controls the characteristics and magnitude of oil recovery by miscible displacement. Standard compositional simulators used to model miscible displacement generally do not include physical dispersion effects and solve the governing equations by first order finite-difference scheme with singlepoint upstream weighting of mobilities. This approach leads to unphysical smearing of fronts (known as numerical dispersion), which is assumed to compensate for the physical dispersion. Unfortunately, this assumption is valid only for one-dimensional problems under very restrictive conditions and can lead to erroneous results in multiple dimensions. The incorporation of physical dispersion in geologically complex models, such as the ones described by non-orthogonal corner-point grids, requires the use of advanced techniques of flux approximation to retain both physical and numerical accuracy. The use of the tensorial form of the permeability or dispersion coefficient becomes a necessity for convective or dispersive transport when flows are not aligned to the principal coordinate axes, which is almost always the case in practical reservoir simulation. In this paper, a new dispersive flux-continuous scheme based on a multi-point control volume procedure is developed to allow the inclusion of the full tensor form of physical dispersion into compositional simulation of miscible displacement on 3-dimensional hexahedron structured corner-point grids. Introduction Gas injection into oil reservoirs results in a number of physical mechanisms that help in mobilizing and extracting the oil. Depending on the pressure, temperature, and the compositions of reservoir oil and the injected gas, immiscible or miscible displacements occur. The mobility ratio of displacing to displaced fluids and the gravity and capillary forces determine the extent of viscous fingering that would take place in a gas displacement process. Often the mass transport is affected by dispersion in different directions due to varying velocity gradients. The oil recovery in a miscible displacement process depends on the size of the mixing zone between the injected fluid and the reservoir oil. For maximum oil recovery at breakthrough to occur, the mixing zone should remain small compared to the reservoir volume so that the oil produced is not diluted by the injected fluid. Ideally in a reservoir with a small mixing zone, for complete oil recovery, slightly more than one reservoir pore volume of injection fluid is required. However, if the mixing zone is large, several reservoir pore volumes of injection fluid may be needed to achieve complete recovery(1). On the other hand, the mixing due to diffusion and dispersion can dampen out viscous fingers in an unstable displacement, leading to increased sweep efficiency. Dispersive mixing is caused by molecular diffusion and mechanical dispersion and is the main part of the mixing in miscible displacements(2). Molecular diffusion is a phenomenon whereby the transport of mass of a species (component) occurs within a single fluid phase from one point to the other in the direction of decreasing concentration.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.008
GPT teacher head0.224
Teacher spread0.217 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations13
Published2005
Admission routes2
Has abstractyes

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