Two-Dimensional Visualization of Heavy Oil Displacement Mechanism During Chemical Flooding
Bibliographic record
Abstract
Abstract There have been numerous laboratory tests conducted in recent years, studying the potential for heavy oil recovery through chemical addition (polymers and surfactants). In these tests, recovery is often understood in terms of pre-water breakthrough and post-water breakthrough improved oil recovery. Pre-breakthrough tests focus on viscous fingering and sweep efficiency from water vs. polymer additives. Post breakthrough tests study the potential for oil recovery after a waterflood has already been run: in these systems surfactants and polymers are added and there is conclusive evidence that chemical additives can be very effective at improving heavy oil recovery at the scale of these core flood systems. The observation made in laboratory core floods is that chemical recovery of oil is achieved under very high pressure gradients. The mechanism proposed in these tests is that chemicals plug water pathways and lead to improved sweep within the core. In reservoir applications of this technology, these same conditions may not be met and it is unclear whether these chemicals will still be as effective in non-linear systems. This study shows tests run in a 2D Hele-Shaw cell, which displaces oil with no pore scale trapping and with many open flow pathways like what is expected in the field. The objective of running tests in this system is to understand the mechanisms of heavy oil recovery and to observe whether chemical additives can still be successful for producing heavy oil in non-linear core systems with no capillary 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".