Optimizing Steam Injection for Eco-Friendly Heavy Oil Recovery: A Pore-Scale Visualization of Chemical Additive Effects
Bibliographic record
Abstract
Abstract Steam-assisted gravity drainage (SAGD) is the primary method for in-situ extraction of heavy oil and bitumen in Canada. However, it (as well as other steam injection methods) suffers from low efficiency, high water and energy consumption, and significant environmental impact. Adding chemicals to steam offers a potential solution by enhancing oil recovery while reducing carbon emissions through lower steam usage. Our previous studies have shown that deep eutectic solvents (DES) and thermally stable non-ionic surfactants improve steam chamber sweep efficiency and microscopic displacement efficiency, respectively. However, their mechanisms and effects in porous media, especially at the pore scale, remain unclear. This study explores the micro-displacement mechanisms of these chemical additives. A porous medium model was created by packing glass beads between two plexiglass plates, which was then saturated with heavy oil at a slow injection rate. The research focused on steam chamber growth dynamics, physicochemical changes at the oil-steam-water-rock interface, and their impact on oil drainage. By comparing oil recovery efficiency, energy efficiency, and carbon intensity of SAGD with and without chemical additives, the effects of DES (formulated in our labs and named as DES 11) and Novelfroth (a commercial non-ionic surfactant) on steam injection efficiency were evaluated. The results demonstrate that DES11 and Novelfroth 190 enhance the recovery rate by 31.1% and 37%, respectively, while reducing carbon emission intensity to one-third to one-half of conventional SAGD. However, their underlying mechanisms differ. Also, the introduction of these chemical additives significantly altered the state of residual oil compared to the base case. Ultimately, the improvement in oil recovery per unit of steam injected in the chemical cases was quantified and linked to a reduction in greenhouse gas emissions during steam injection.
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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".