Pore-Level Simulation of Heavy Oil Reservoirs; Competition of Capillary, Viscous, and Gravity Forces
Bibliographic record
Abstract
Abstract The advancing high resolution scanning technology has formed a concrete basis for simulation of pore events within microstructures. Interactions between capillary, gravity, and viscous forces result in complex flow phenomena affecting pore-scale physics and phase distributions during different displacement scenarios. Consequently, a myriad of techniques has been proposed to deal with all effective forces and dynamically simulate pore scale multi-phase flow physics. In this regard, the significance of each force, particularly viscous forces, on pore-level flow morphology is not yet well studied. Here, the Navier-Stokes equation along with a VOF volume tracking advection equation is applied for simulation of two-phase displacement scenarios considering gravity, capillary, and viscous forces. A pore-throat pair is used as a simple pore-level geometry to conduct a comprehensive sensitivity analysis and investigate the effect of viscosity, IFT, contact angle and velocity on the trapping amount of non-wetting phase. The results are in good agreement with available experimental data and confirm that in pore-level transport phenomena, the amount of residual trapping is a function of pore-throat geometry and wettability and is not affected greatly by interfacial tension or differences of viscosity. The analysis also demonstrates that within microscale porous media images the gravity role is negligible due to low Bond number values. A pore morphology-based quasi-static approach is then applied to a multi-pore micro-tomographic sandstone image to simulate drainage, and imbibition processes and investigate the effect of geometry, contact angle, and IFT on the amount of heavy oil residual trapping.
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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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".