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Record W2035012689 · doi:10.2118/05-05-02

Modelling Physical Dispersion in Miscible Displacement-Part 2: Validation, Numerical Tests, and Applications

2005· article· en· W2035012689 on OpenAlexafffund
Vijay Shrivastava, Long X. Nghiem, R.G. Moore, T. Okazawa

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2005
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsImperial Oil (Canada)University of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDispersion (optics)Displacement (psychology)MechanicsDiffusionNumerical diffusionMixing (physics)Materials scienceComputer scienceThermodynamicsPhysicsOptics

Abstract

fetched live from OpenAlex

Abstract A new method for the incorporation of physical dispersion into miscible displacement models was developed in an accompanying paper(1). This paper presents the numerical tests for validation of the proposed formulation, and applications for demonstration of the method in certain practical situations. The examples include miscible displacement in a linear system, miscible displacements in a quarter of a five-spot pattern, and the application of the proposed method in the simulation of vapour extraction (VAPEX) process. Introduction Molecular diffusion and mechanical dispersion are the main mechanisms responsible for gas-oil mixing that occurs in miscible displacements. Together, these mechanisms are called physical dispersion and appear as the coefficient of the second order derivative of concentration in the convection-diffusion equation. The dispersion coefficient is tensorial in nature and requires special treatment for inclusion in the molar conservation equations, especially for models with complex reservoir geometries. A new method based on a multi-point control-volume procedure was developed for incorporation of physical dispersion in miscible displacement modeling(1) on 3D hexahedron structured corner-point grids. The method was implemented in a compositional simulator that has a provision for a higher order scheme(2) to reduce numerical dispersion. This paper seeks to address validation, testing, and application aspects of the implementation. These are done on few single and multi-dimensional miscible displacement problems. The methodology consists of using full tensor formulation of physical dispersion based on a flux continuous multi-point control-volume procedure in conjunction with a scheme for the reduction of numerical dispersion. For the latter, wherever necessary, a higher order scheme with a total variation diminishing (TVD) flux limiter(2) is used. The thermodynamic calculations are done with the Peng-Robinson equation-of-state(3). Model Validation Validation of the Proposed Formulation With Analytical Solutions These runs compare numerical solution obtained from the proposed method with the analytical solution for miscible displacement in models with simplified geometry and well-defined boundary and initial conditions, and under assumptions necessary for arriving at the analytical solution. First, the analytical solutions are briefly presented followed by the model description and numerical solution with the proposed method. The Analytical Solution to 1D Convection-Diffusion Equation The flow of a component in a phase can be described by the convection- diffusion (C-D) equation in 1D, dimensionless form(4): Equation 1–5 (available in full paper) where C, CI, and CJ indicate current, initial, and injected concentrations, and L denotes the distance over which longitudinal dispersion, De, is measured. The dimensionless time, tD, corresponds physically to the number of cumulative pore volumes injected into the medium. The other variables are defined in the nomenclature. Equation (1) assumes flow of a single phase incompressible fluid in permeable media and ideal mixing. The analytical solution of Equation (1) for miscible displacement of an initially uniform fluid distribution in a finite length L under the boundary conditions: Equation 6a-6c (available in full paper) has the form of an infinite series of error functions where successive terms arise from the superimposed reflections at the outlet.

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)0.002
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: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.214
Teacher spread0.208 · 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
GenreEmpirical

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

Citations3
Published2005
Admission routes2
Has abstractyes

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