Optimization of Steam Assisted Gravity Drainage in McMurray Reservoir
Bibliographic record
Abstract
Abstract Many field tests of the steam assisted gravity drainage (SAGD) process have been conducted and have shown that the process is a technically effective one at extracting oil from heavy oil and bitumen reservoirs. However, it has not been firmly established whether the technology is operated at optimized conditions to yield maximum economic returns. This is especially important because typically SAGD depends on the combustion of natural gas to generate steam and this is the dominant cost. The cost of natural gas can be significant when natural gas prices are high. This research evaluates the use of a genetic algorithm optimization scheme to control a commercially available thermal reservoir simulator in order to optimize the steam injection strategy to reduce the cumulative oil to steam ratio (cSOR). The reservoir description is typical of that from a low to medium quality Athabasca reservoir. The results show that the injection strategy can be altered to reduce the cSOR up to 50% from a uniform injection pressure strategy to 1 after the steam injection strategy has been optimized. The optimized profile has high steam injection pressure at the beginning of the process before the steam chamber reaches the top of the oil-rich zone. Before the chamber reaches the oil pay, with high injection pressure, the saturation temperature is high and there are no thermal losses to the overburden. After the chamber reaches the top of the formation, the injection pressure is lowered throughout the remainder of the process. This reduction of injection pressure implies that the saturation temperature falls and consequently the losses to the overburden are lowered. Thus the overall thermal efficiency of the process is enhanced. The optimized strategy is compared to processes operating at 1,000 and 2,000 kPa constant injection pressure. Introduction Steam assisted gravity drainage (SAGD) has now been extensively tested and put into commercial production in the Athabasca and Cold Lake regions of Alberta(1–5). The majority of existing SAGD projects are based in Alberta, Canada: more than nine are located in the Athabasca region (the McMurray formation); one in the Peace River region, (the Bluesky formation); four are in the Cold Lake region (the Clearwater formation and the Grand Rapids formation); and, five are in Saskatchewan (the Grand Rapids formation). The SAGD process, shown in Figure 1, was developed by Butler(2) while at Imperial Oil in the late 1970s. The process consists of two aligned horizontal wellbores. Steam is injected into the top one, whereas reservoir fluids are produced from the bottom one. The process is non-cyclic; that is, steam is continuously injected and fluids are continuously produced. Around and above the injection well, a steam chamber grows. The injected steam flows into the steam chamber and eventually comes into contact with oil sand at its edge. The steam then releases its latent heat to the oil sand, the oil heats up, its viscosity drops, and it flows (with water condensate) under gravity down the inclined chamber edge to the production well.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".