Effectiveness of Natural Gas Condensate as a Viable Solvent in ES-SAGD Processes: An Experimental Investigation Using a 3-D Physical Model
Bibliographic record
Abstract
This study presents an experimental evaluation of the ES-SAGD process to better understand the recovery mechanisms, determine the optimized solvent concentrations, and enhance the overall process efficiency. Conducting three-dimensional physical model experiments (3DPMEs) of ES-SAGD poses significant challenges due to their complexity, cost, labor intensity, and time requirements. In this work, 3DPMEs were performed using varying concentrations of natural gas condensate as a solvent, chosen for its field availability and cost-effectiveness compared to pure solvents. A baseline 3DPME of conventional SAGD was also conducted for comparative purposes. Key aspects measured in this work included oil recovery factor, oil rate, water cut, cumulative steam–oil ratio (cSOR), cumulative gas produced and its composition, gas–oil ratio, and energy–oil ratio. Results demonstrate that adding natural gas condensate in ES-SAGD significantly improves bitumen recovery rates over baseline SAGD. The optimal solvent concentration was identified as 10% condensate with steam, which maximized oil production rates and reduced water cut. The cSOR for 10% solvent was approximately 2.83, compared to 7.6 for conventional SAGD experiment at 2 pore volume injected. This study highlights the potential of solvent-aided thermal recovery to significantly reduce the environmental impact of the oil sands industry, offering a pathway to lower greenhouse gas emissions and capitalize on carbon tax incentives.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".