Mobilization of Trapped Residual Oil via Secondary SAGD with Propane
Bibliographic record
Abstract
Abstract Solvent- Steam Assisted Gravity Drainage (S-SAGD) processes for bitumen extraction are proposed to reduce the environmental impact of steam injection. S-SAGD processes require more research due to the unknowns of solvent-bitumen interaction and the desire to reduce the cost of steam and solvent utilized. This study investigates propane-SAGD (P-SAGD) and propane-steam flooding (P-SF) performance for the recovery of a Canadian bitumen from Alberta with 9.6 API gravity, 290,500 cP viscosity (at 25 °C), and 21.7 wt% asphaltenes (n-pentane insoluble) content. Three two-dimensional SAGD experiments (one SAGD and two P-SAGD at two different propane doses) and three one-dimensional flooding experiments (propane, steam, and propane-steam) were conducted. By comparing 2D experiments with 1D, we were able to analyze the effect of continuous steam flow and steam chamber development on process performance in microscopic scale. Water and asphaltenes contents of produced oil were measured. It has been observed that the steam chamber development with propane coinjection enhanced the oil production, however, led to delay in oil production compared to the steam flooding case. Thus, we also tested first steam injection until achieving the communication between the injector and producer in SAGD configuration and then, switching to steam-propane coinjection. After allowing the steam-bitumen interaction first, propane injection did not result in severe water-in-oil emulsion formation. Moreover, lesser permeability reduction due to asphaltenes deposition was observed. The application of propane-SAGD as follow up to SAGD improved the process by the mobilization of trapped residual oil and enhanced the quality of produced oil by minimizing the formation of water-in-oil emulsion.
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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".