Feasibility Study of Using Polymer to Improve SAGD Performance in Oil Sands with Top Water
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Bibliographic record
Abstract
Abstract In western Canada, there are significant amounts of oil sands reserves that have little or no cap rock with a top water zone (Alturiki et al. 2011); because of huge heat loss, conventional SAGD process is uneconomical when it is directly applied in this type of reserves. In this study, it is proposed that high temperature polymer can be injected into the bottom of the top water zone to establish a stable high viscosity layer in order to prevent steam from leaking to the top water zone. Lab tests were first conducted to screen the polymers. In order to select a proper polymer which was able to have stable viscosity under high temperature, viscosities of different polymers at different temperatures were measured; and concentration of the selected polymer was optimized. Then numerical simulations were performed to evaluate the feasibility of using the selected polymer to improve SAGD performance in oil sands with top water. The numerical simulation model was based on Athabasca oil sands reservoir. In this formation, the top water zone was around 94 meters, while the reservoir thickness was about 30 meters. The vertical permeability was 50 mD and 1,400 mD for the top water zone and the oil zone, respectively. And the porosity was 10% for the top water zone and 30% for the oil zone. The effect of the polymer injection strategy including the polymer injection parameters, such as polymer slug size, injection rate, injection time and well distance on the performance of SAGD process was studied. The numerical simulation results suggested that, polymer injection was able to block the heat from leaking to the top water zone. With polymer injection, the cSOR can be reduced from 8.5 m3/m3 to 4.8 m3/m3, while for the case without top water, the cSOR was 3.8 m3/m3. This indicates that polymer injection is technically feasible to improve SAGD performance in oil sands with top water.
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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 it