Successful Application of Discontinuous SAGD Technique to Maximize Oil Recovery in Alberta's Oil Sands
Bibliographic record
Abstract
Abstract The steam assisted gravity drainage (SAGD) process has proven to be an effective thermal recovery method for heavy oil and bitumen production. Recently, some innovative techniques based on the background SAGD operation have been applied such as conventional SAGD, Fast-SAGD, Hybrid SAGD, FA-SAGD. However, there has been much dispute over the question of economical efficiency due to high capital investment, operating costs, and the fluctuation of oil and gas prices. The integration of economic and technical aspects for SAGD performance plays an important role in field operation. Main problems to solve are how to design optimal operating conditions for a reasonable steam requirement with certain injection pressure to maximize economic feasibility under reservoir conditions. The previous literatures implemented sensitivity analysis and optimization of SAGD performance by classical methods based on numerical simulations lead to a lack of confidence level and ignored interactions effects between considered parameters, which may cause low efficiency issues in a field operation. In addition, the SAGD economic model in previous studies have not fully information with limited consideration on few factors. These restrictions can be avoided by the application design of experiment and response surface methodology to determine the optimal operating conditions for the production prediction. Then, discontinuous SAGD technique was operated to control the amount of injected steam at several specific injection time intervals. This study represented that the discontinuous SAGD performance is applied to three major formations of Alberta’s oil sands. The results showed that both oil recovery factor and economic profit are much higher than other techniques, especially the effective at deeper burial reservoirs of Clearwater and Bluesky formations. Moreover, amount of injected steam and water cut reduced significantly to cause lower operating costs as steam cost, led to increase NPV as well as minimal environmental damages.
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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 | 0.001 | 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 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".