A Novel Approach for Incorporating Physical Dispersion in Miscible Displacement
Bibliographic record
Abstract
Abstract Physical dispersion is one of the main mechanisms responsible for controlling the gas-oil mixing that occurs in a miscible flood process. Many conventional reservoir simulators do not explicitly account for the physical dispersion and presume that it may be compensated by numerical dispersion arising out of the finite difference scheme with single point upstream weighting of mobilities for the reservoir grid block sizes used in field-scale simulations. This assumption may lead to erroneous results. The multi-point flux approximation (MPFA) schemes developed in recent years provide improved treatment of the convective flux and allow the handling of tensorial permeabilities for non-uniform and skewed grids. These grids 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 the permeability in the convective flux. We have applied a multipoint control-volume method together with a total variation diminishing (TVD) scheme to calculating the dispersive flux in a compositional simulator. The TVD scheme was used to minimize the effect of front smearing caused by numerical dispersion. This paper presents a method for calculating the full physical dispersion tensor in a compositional simulator using corner-point grids. The proposed formulation accurately handles dispersive flux for non-orthogonal grids, and along with the TVD scheme provides a means for distinguishing physical dispersion from numerical dispersion. A number of cases are presented to show the improvements in simulation results that could be obtained with the proposed method.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".