Effect of Oil Viscosity on Heavy-Oil/Water Relative Permeability Curves
Bibliographic record
Abstract
Abstract For heavy oil reservoirs, the oil viscosity usually varies dramatically during production processes, such as thermal process or solvent injection. This paper presents an investigation of the effect of oil viscosity on relative permeability curves for heavy oil-water systems. Unsteady-state displacement tests were conducted in sandpacks under a typical injection flow rate in a heavy oil recovery process. A series of crude oils with a wide range of viscosities were used in the measurements. Large pore volumes of water were injected to minimize the errors caused by the extrapolation of the recovery data. History matching was used to obtain the relative permeability curves, in which capillary pressure was included. It was found that, for the same injection flow rate, heavy oil-water relative permeability curves systematically shifted with oil viscosity. With increasing oil viscosity, the residual oil saturation increased and the oil and water relative permeabilities decreased at the higher water saturation range. Irreducible water saturation tended to decrease with increasing oil viscosity. Micromodel experiments were conducted to visually investigate the difference in the flow behaviour between heavy oil-water and light oil-water systems. Interacting capillary bundle models were used to analyze the impact of oil viscosity on the residual oil saturation. This work aids in the laboratory measurement and determination of the representative relative permeability curves for heavy oil-water systems, as well as in the proper use of relative permeability curves in reservoir simulation for heavy oil development.
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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 | 0.002 |
| 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 | 0.001 |
| 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".