High-Temperature Solvent Injection for Heavy-Oil Recovery From Oil Sands: Determination of Optimal Application Conditions Through Genetic Algorithm
Bibliographic record
Abstract
Summary Our recent experimental studies on superheated solvent injection for heavy-oil recovery showed that when a solvent is injected into the reservoir, the process is highly sensitive to pressure and temperature. The effects of these parameters on the recovery factor (RF) are accentuated when the operating conditions are closer to the saturation curve of the solvent injected. This paper investigates this process and formulates the optimal field-scale application conditions that yield the maximum profit, as a continuation of previous work. To achieve this, a hypothetical field-scale numerical model was constructed, and the key parameters identified through the aforementioned sensitivity analysis were incorporated. Then, the injection process was simulated for a two-horizontal injection/production pattern. An optimization study was performed to identify the relative contributions of the effective parameters (pressure, temperature, and injection rate) and to propose an optimal application scheme with a genetic algorithm (GA). The critical pressure and temperature yielding maximum production and highest profit considering solvent retrieval were defined for different injection rates and application scenarios. Our results indicate that, at the end of the hot solvent-injection process, an important volume of solvent is left in the reservoir, and its volume depends on the injection–production scheme selected. Nevertheless, if the project is performed under appropriately selected operational parameters (obtained through the optimization processes) and followed by the proper process to retrieve the solvent from the reservoir (low-temperature steam or hot-water applications), it can make the hot solvent-injection process profitable.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".