Optimal Control Of Atmospheric Air Injection For Enhanced Oil Recovery
Bibliographic record
Abstract
Gas-based enhanced oil recovery (EOR) processes rely on the injection of gases such as carbon dioxide, nitrogen, and natural gas into heavy oil reservoirs to reduce inherent oil viscosity. Although these processes are very promising, they face the problem of limited and costly gas supply. This study investigates the conditions, specifically temperature variation, under which freely available air at low temperatures, low pressures, and non-reactive environments for heavy oil recovery. To that end, preliminary experiments are carried out to demonstrate the possibility of beneficial effects of air temperature variation with time. Furthermore, this research aims to utilize the theory of optimal control to determine optimal air temperature versus time function to maximize the heavy oil recovery. For this purpose, the conditions necessary for optimal control are derived and utilized in a computational algorithm. The preliminary experiments are executed by injecting air into a lab-scale heavy oil reservoir at different pressures (0.169-0.514 MPa absolute) and temperatures in the range of 25-90oC. Reservoirs of four different permeabilities (40-427 Darcy) are used in experiments. When air is injected with a periodic temperature variation between 90oC and 75oC that has an average of 78oC, the recovery is increased from 58.2% to 69.1% of the original-oil-in-place (OOIP) in comparison to that using constant temperature air injection at the maximum temperature of 90oC. That is a considerable improvement of oil recovery by 18.6%. Furthermore, utilizing optimal control the optimal interfacial temperature versus time (control policy) is determined between 90oC and 82oC, which registers 20.66% increase in the oil recovery in comparison to that at the constant temperature of 90oC. The accuracy of optimal control is experimentally validated. The results show that the average relative difference between the predicted heavy oil recovery and the experimental value is a low value of 1.82%.
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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".