Numerical Evaluation of Hydrocarbon Additives to Steam in the SAGD Process
Bibliographic record
Abstract
Summary Heavy oil and bitumen are expected to become increasingly important sources of fuel in the coming decades. Steam assisted gravity drainage (SAGD) is a commercially viable and widely used recovery technique for heavy oil and bitumen. However, it remains an expensive technique and requires large energy input in the form of steam. Energy intensity of SAGD, as well as environmental concerns such as fresh water usage and CO2 emission, make it imperative to find new oil extraction technologies. Coinjecting a hydrocarbon additive with steam offers the potential of higher oil rates and recoveries with lower energy and water consumption. A reservoir simulation study using a 20X12X15 3D Cartesian model and Athabasca fluid and reservoir properties was conducted to evaluate this process. The role of hydrocarbon additive in the steam chamber and its effect on the performance of SAGD was investigated. Simulation results revealed the parameters that will have the greatest impact on the process performance and indicated the effectiveness of each hydrocarbon additive in improving the performance of SAGD. The results also showed that selecting the most suitable hydrocarbon additive depends on the operating conditions as well as the original reservoir fluid composition.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".