Onset of Convective Mixing at the Edge of Steam Chamber in Steam-Solvent Recovery of Heavy Oil and Bitumen
Bibliographic record
Abstract
Abstract With increasing world demand for energy, greater attention has been placed on the exploitation of the huge existing resources of heavy oil and bitumen. Although thermal in-situ recovery methods such as steam assisted gravity drainage (SAGD) have been very successful in exploiting such resources, the thermal efficiency of SAGD, its greenhouse gas emissions and water requirements remain major concerns. Co-injection of solvent with steam shows promise for enhancing oil rates as well as reducing energy and water consumption with correspondingly lower environmental impacts. In hybrid steam-solvent methods, there is a balance between the solubility of the solvent and its ability to reduce bitumen viscosity. Proper selection of the solvent for the reservoir operating conditions is key for optimizing process efficiency and maximizing performance improvement over the steam-only method. Convective mixing at the edge of the steam chamber enhances heat and mass transfer rates which increases oil mobility and production rate. In this study, the convective mixing at the steam-bitumen interface is examined using theoretical stability analysis of the thermal-solvent boundary layer. Several alkane solvents were compared based on the time required for the onset of the buoyancy-driven instabilities in the system. The results show that there is a higher degree of convective mixing for some intermediate solvents, which is in agreement with reported laboratory and simulation results. The onset of convective mixing and the wavelength of the instabilities are obtained as a function of reservoir and fluid properties for various solvents. These results can aid in the screening and selection of appropriate solvent additives to steam for a given reservoir and bitumen properties; also this analysis can be applied for mixtures of solvents to optimize the overall efficiency of the steam-solvent recovery method.
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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".