Optimization of the Steam Alternating Solvent Process Using Pareto-Based Multi-Objective Evolutionary Algorithms
Bibliographic record
Abstract
Abstract Steam alternating solvent (SAS) process has been proposed as a more environmentally friendly alternative to traditional steam-based processes for heavy oil production. It consists of injecting steam and a non-condensable gas (solvent) alternatively to reduce the oil viscosity. However, optimizing multiple process design (decision) variables is not trivial since multiple conflicting objectives (i.e., maximize the recovery factor, reduce steam–oil ratio) must be considered. Three different multi-objective evolutionary algorithms (MOEAs) are employed to identify a set of Pareto-optimal operational parameters. A multi-objective optimization (MOO) workflow is developed: first, a 2D reservoir model is constructed based on the Fort McMurray formation. Second, a sensitivity analysis is performed to identify the most impactful decision parameters. Third, two response surface (proxy) models and three different MOEAs are employed and compared. This paper is the first to compare different MOEAs for optimizing a wide range of operational parameters for the SAS process. The results show that if more steam is injected, extending the steam cycle duration is preferable. Conversely, if more solvent is injected, it is recommended to start with injecting a solvent with high propane concentrations over short cycles and switch to lower propane concentrations over long cycles near the end.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".