Visualization of Viscous Coupling Effects in Heavy Oil Reservoirs
Bibliographic record
Abstract
Abstract Despite the many publications about the production mechanisms of heavy oil reservoirs, there is still no consensus as to what is behind the anomalous production rates observed in some reservoirs in Venezuela and Canada. Many authors attribute the recovery of a much higher fraction of the original oil in place to a special phenomenon associated to the solution-gas drive mechanism, known as "foamy" or "bubbly oil", present in heavy oil reservoirs. However, the "foamy oil" behavior cannot clearly explain the mechanism of the observed oil production rate enhancement. Considering the physical principles of two-phase flow through porous media, the viscous coupling or momentum transfer between the flowing phases would appear as a hidden driving mechanism to explain the improved oil mobility. The use of capillary models has provided a new insight into the effect of the viscosity ratio on relative permeabilities and the importance of water lubrication in heavy oil-water two-phase flow. However, despite the models predictions show that the viscosity ratio affects the relative permeabilities, especially in systems involving very viscous oil, the lack of experimental results makes it difficult to support the theoretical observations. As a result of the considerable ambiguity in the literature regarding the viscous coupling effects, the focus of this work is to provide new experimental results on the effect of the viscosity ratio on the oil mobility at the pore scale. Flow visualization experiments are conducted in a 2D etched network micromodel. Observations and measurements are presented.
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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 | 0.000 |
| 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) | 0.001 | 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".