Investigation of Post-Breakthrough Heavy Oil Recovery by Water and Chemical Additives Using Hele-Shaw Cell
Bibliographic record
Abstract
Abstract There has been a large number of laboratory tests run in the past, looking at recovery of heavy oil by chemical flooding (polymer, surfactant and SP or ASP systems). These tests have all shown a potential for significant incremental oil recovery from chemical injection compared to water alone. The higher oil recovery was generally achieved under high pressure gradients that were generated during chemical injection. As such, the mechanism proposed in all of these studies is that of water channel blockage and improved sweep from chemical injection. In actual reservoir applications of chemical flooding, the impact of these water pathway blockages may be much less important than what is observed in the linear laboratory scale tests, so the actual mechanisms of chemical flooding in real systems may not be properly represented. In this project, an experimental investigation is performed using a 2D visual Hele-Shaw cell, to visualize production mechanisms during the chemical flooding process. The 2D nature of the system allows for an understanding of whether chemicals sweep incremental oil from along the same channels as the pre-formed water or if new displacement channels are formed. The flow of a fluid in such models also represents flow without any pore-scale trapping, so observations can be made regarding whether trapping and fluid re-diversion are the only ways that these chemicals can yield improved oil recovery, or if simple alteration of the mobility ratio without additional trapping will already lead to incremental oil.
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.
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".