A Novel Approach for Incorporating Physical Dispersion in Miscible Displacement
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it