Influence of Pressure Difference Between Reservoir and Production Well on Steam-Chamber Propagation and Reservoir-Production Performance
Bibliographic record
Abstract
Summary Steam-assisted gravity drainage (SAGD) is the most-effective thermal recovery method to exploit oil sand. The driving force of gravity is generally acknowledged as the most-significant driving mechanism in the SAGD process. However, an increasing number of field cases have shown that pressure difference might play an important role in the process. The objective of this paper is to simulate the effects of injector/producer-pressure difference on steam-chamber evolution and SAGD production performance. A series of 2D numerical simulations was conducted using the MacKay River and Dover reservoirs in western Canada to investigate the influence of pressure difference on SAGD recovery. Meanwhile, the effects of pressure difference on oil-production rate, stable production time, and steam-chamber development were studied in detail. Moreover, by combining Darcy's law and heat conduction along with a mass balance in the reservoir, a modified mathematical model considering the effects of pressure difference is established to predict the SAGD production performance. Finally, the proposed model is validated by comparing calculated cumulative oil production and oil-production rate with the results from numerical and experimental simulations. The results indicate that the oil production first increases rapidly and then slows down when a certain pressure difference is reached. The pressure difference has strong effects on steam-chamber-rising/expansion stages. However, at the expansion stage, lower pressure difference can achieve the same effect as high pressure difference. In addition, it is shown that the steam-chamber-expansion angle is a function of pressure difference. Using this finding, a new mathematical model is established considering the modification of the expansion angle, which (Butler 1991) treated as a constant. With the proposed model, production performance such as cumulative oil production and oil-production rate can be predicted. The steam-chamber shape is redefined at the rising stage, changing from a fan-like shape to a hexagonal shape, but not the single fan-like shape defined by (Butler 1991). This shape redefinition can clearly explain why the greatest oil-production rate does not occur when the steam chamber reaches the caprock. Literature surveys show few studies on how pressure difference influences steam-chamber development and SAGD recovery. The current paper provides a modified SAGD production model and an entirely new scope for SAGD enhanced oil recovery (EOR) that makes the pressure difference a new optimizable factor in the field.
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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 | 0.001 |
| 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".