Noncontinuous Steam Injection Optimization for SAGD Process for Improving Heavy Oil Recovery
Bibliographic record
Abstract
Abstract Several processes for improved oil recovery are thermal and are based on steam injection. Through these methods is possible to heat the reservoir, reducing oil viscosity and increasing the oil-phase mobility, allowing a better oil displacement in reservoir and increasing swept efficiency. Nowadays, one of the most promising thermal recovery technologies is Steam Assisted Gravity Drainage (SAGD). In this process, two horizontal wells, separated by a vertical distance are placed near formation bottom. Horizontal top well is used for steam injection, which creating a steam chamber which grows upper and to surroundings allow heat transfers between steam and oil by conduction. Bottom well is used for oil production. SAGD process appears to be technically attractive, due to the high recovery and highs oil rates and oil-steam ratio. This process was applied in country such U.S.A., Canada and Venezuela. In this work, it was idealized a reservoir with some northeast Brazilian characteristics in a homogeneous model. It was done an optimization of steam rate, in a non continuous form, injected steam for several time periods. For the optimization study was also realized a net present value study and it was compare to a process with continuous steam injection. All the cases studied were done using the software STARS from CMG (Computer Modelling Group). This study showed that, in SAGD process, steam requirement can be reduced by injecting it in a non continuous form, alternating steam injection with stops at several time intervals. It was possible optimized these intervals minimizing heat losses and improving oil recovery. The optimal time interval was found at six months that mean, it can injected steam for six months and then stops the steam injection for next six months. When it was compare to a system with continuous steam injection, was founded that this system had a lower net present value that the system with steam injection with stops. It was founded that was possible to reduce water production, with an improved of oil recovery. In this work was obtain a way to optimized steam rate, minimizing heat losses and increasing net present value, in reservoirs with some Brazilian Northeast Basin characteristics, and that become important, because it is necessary to improved heavy oil recovery with minimal environments damages, and with lower production cost.
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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".