Optimal Application Conditions of Steam-Solvent Injection for Heavy Oil/bitumen Recovery from Fractured Reservoirs: An Experimental Approach
Bibliographic record
Abstract
Abstract Although the previous static experiments provided critical information as to the existence of a critical temperature range that yields the maximum heavy-oil recovery during steam/solvent injection, dynamic experiment are needed to account for the relationship between the solvent introduced into the system and heavy-oil recovery. We conducted a series of dynamic experiments in which liquid (heptane) solvent was injected into a heavy oil saturated rock matrix, surrounded by a fracture with and without pre-thermal injection. Water-wet rock matrix (sandstones) was saturated with heavy oil and placed inside a core holder. Next, the system was placed into an oven and maintained at constant temperature conditions. Then, either hot solvent (superheated to be in vapor phase) or cold solvent was introduced into the system through the fracture at a constant rate. Pressure and temperature was continuously monitored along the core and the properties of oil and liquid condensate from gas produced were measured and analyzed. This scheme was repeated for a wide range of temperature conditions. The first requirement for a successful application is that the solvent should diffuse into matrix effectively before it breaks through and improves gravity drainage of oil by dilution. The second requirement is solvent retrieval. The retrieval of the solvent during solvent injection phase and post-thermal method (steam or hot-water) injection performed at the near-boiling point temperature of the solvent was monitored. Our results and observations indicate that there exists a critical temperature and injection rate that yields a maximized oil recovery and solvent retrieval.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".