Experimental Study of the Mechanisms in Heavy Oil Waterflooding Using Etched Glass Micromodel
Bibliographic record
Abstract
Abstract In western Canada, there have been more than 300 heavy oil waterflooding projects. Most of these projects displayed good economical and efficient variability even though they were operated in marginal pools. Although waterflooding of heavy oil has almost 50 years history, its mechanisms, especially in the situation of high oil water viscosity ratio, are still not well understood. In the situation of high viscosity ratio, fractional flow theory does not work because of severe water fingering and other mechanisms that are different from conventional waterfloods. The operation strategies of heavy oil waterflooding, such as water injection rate, injection pressure and VRR, are still under controversy. In a water-wet environment, waterflooding (water displacing oil) represents a process of water imbibition. In this paper, the water imbibition mechanisms and their effects on the heavy oil recovery are studied using a water-wet micromodel. The effects of time, viscosity ratio and water injection rate on the imbibition rate are also studied. The imbibition rate of water was found to be proportional to the reciprocal of the square root of time, and inversely related to oil viscosity. The effects of injection rate on imbibition rate are complicated. At low injection rates, waterflooding becomes more efficient, and significant volume of oil is produced discontinuously. Images of the imbibition process were recorded and analyzed from visual micromodel studies. Water broke through quickly because of water fingering, and a considerable portion of recovery comes from post-breakthrough production of oil, under high water cuts. In the cases of low rate water injection, water imbibed into the original oil region perpendicularly to the water channel. In this stage, capillary imbibition was a key factor. Water film thickening and snap-off were the two main mechanisms that made water imbibition work. Emulsification was also another important mechanism observed, with W/O emulsions primarily being formed.
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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".