Design of SOS-FR (Steam-Over-Solvent Injection in Fractured Reservoirs) Method for Heavy-Oil Recovery Using Hybrid Optimization Framework
Bibliographic record
Abstract
Abstract In order to reach the ultimate heavy oil and bitumen recovery with minimal cost, efficient and optimized design for recovery processes operation strategies is necessary. Despite the amount of the heavy oil and bitumen reserves around the world, the production is limited due to the production development difficulties such as high cost, complex processes, and environmental concerns. Many design and performance evaluation studies published in the literature combine numerical simulation with graphical or analytical techniques; however, only few design elements are handled due to the difficulties of handling large number of factors. Due to the high computation requirements, limited efforts that integrated the simulation exercise with global optimization algorithms to handle more design elements. In this paper, a hybrid global optimization framework is used to optimize the design of a new process called Steam-Over-Solvent in Fractured Reservoirs (SOS-FR) proposed by Al-Bahlani and Babadagli (2008, 2009a-b, 2011a-b). The hybrid framework integrates genetic algorithm with orthogonal arrays and response surface proxies for better convergence behavior and higher computational efficiency. The SOS-FR technique consists of a heating phase using steam injection, subsequent solvent injection, and low temperature steam injection for solvent retrieval and additional oil recovery. Solvent injection can be continuous or cyclic where the solvent is injected, soaked, and then fluids are produced. This paper studies both scenarios over single and multiple matrix field scaled reservoirs by adjusting the injections’ durations and rates. As a result, about 30 design elements for four base benchmark models are optimized, and the profit and efficiency is doubled comparing with the benchmark models using optimal injection scheme suggested by our optimization framework.
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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 | 0.000 |
| 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) | 0.000 | 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".