Experimental Analysis of Dimethyl Ether and Natural Gas Condensate Performance in Expanding Solvent-Steam Assisted Gravity Drainage Process
Bibliographic record
Abstract
Summary We report an experimental study comparing the recovery performance, steam usage efficiency, and environmental metrics of dimethyl ether (DME) and natural gas condensate (NGC) as solvents in the expanding solvent-steam-assisted gravity drainage (ES-SAGD) process. The experiments were conducted using a novel fast-screening experimental setup at 220°C and 336.4 psia. DME was evaluated at 0.64 mol%, 1.31 mol%, and 2.00 mol%, while condensate concentrations were 5 vol%, 10 vol%, and 15 vol%. Under the studied conditions, 1.31 mol% DME recovered 83% of the original oil in place (OOIP), representing 26% improvement over conventional SAGD, which achieved 66% OOIP recovery, with significantly improved steam usage efficiency throughout the 210-minute experimental period, reducing the average cumulative steam-to-oil ratio (cSOR) from 13.49 to 8.6. NGC performed optimally at 10 vol%, recovering 82% of the OOIP and achieving the most favorable steam usage efficiency among all tested cases. The study identified an optimal concentration that maximizes recovery for both solvents. Analyzing key performance indicators, including bitumen recovery, bitumen production rate, steam-to-oil ratio (SOR), gas production rate, and gas composition, revealed that both solvents demonstrate significant environmental benefits. Coinjection of NGC with steam at 10 vol% resulted in a 13.69 GJ/m3 reduction in energy consumption and decreased emissions from 20 kgCO2/m3 to 10 kgCO2/m3 of oil produced relative to SAGD. The study reveals that while DME offers slightly higher oil recovery potential, NGC provides more consistent performance across different concentrations and better overall efficiency metrics, making it a promising option for commercial applications. The results are primarily comparative due to the limited size and homogeneity of the physical model and the simplified heat transfer; consequently, extrapolating the performance metrics should be done cautiously. This study offers critical insights into solvent selection and optimization for ES-SAGD processes, providing the oil sands industry with practical strategies to enhance efficiency while tackling energy and environmental challenges.
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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 | 0.001 |
| 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".