Reducing the Energy and Steam Consumption of SAGD Through Cyclic Solvent Co-Injection
Bibliographic record
Abstract
The amount of oil that is contained in the Canadian oil sands represent the third largest oil accumulation in the world. Approximately half of the daily oil production from the oil sands comes from mining processes and the other half is produced mostly using steam assisted gravity drainage (SAGD). This method is effective at reducing the viscosity of the oil. However, the generation of steam requires a significant amount of energy. Thus, there is an ongoing effort to reduce the energy needed to produce oil from the oil sands. In this article the intermittent injection of a solvent, along with steam, is investigated as a means of reducing the amount of energy required to extract oil from the Canadian oil sands. A simulation-based study examined the effect of the type of solvent, the cycles’ duration, the solvent concentration and the number of cycles. The simulations covered a time span of 10 years during which several different solvents (methane, ethane, propane, butane, pentane, hexane, and CO2) were injected under varying injection schedules. The solvents that were investigated are compounds that are likely to be readily available at a heavy oil production site. The solvent injection periods ranged from six to 24 months in length. The results reveal that SAGD combined with intermittent injection of hexane resulted in the most significant improvement to the cumulative oil production and in the cumulative energy-oil ratio (cEOR). Compared to SAGD without solvent injection, the cumulative oil production was increased by 45% and the cEOR was reduced by 23%. It was also seen that the performance of the proposed process is highly dependent on the resulting physical properties of the solvent-bitumen mixture. Finally, a simplified economic analysis also identified SAGD with intermittent hexane injection as the scheme that resulted in the highest net present value. Compared to SAGD without solvent injection, the intermittent injection of hexane led to an 85% increase in the net present value.
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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 | 0.000 | 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".