A Study of DME-Steam Co-Injection Using a Large-Scale Physical Model
Bibliographic record
Abstract
Abstract Dimethyl ether (DME) as a water-soluble solvent has been studied as a potential additive to steam for improving the energy efficiency of steam-assisted gravity drainage (SAGD). The main objective of this research was to study in-situ flow characteristics and energy efficiency of DME-SAGD using a large-scale physical model. Results from DME-SAGD were compared with the control experiment of SAGD with no solvent injection using the same experimental setup. The main novelty of this research lies in the experimental data that demonstrated enhanced bitumen drainage by DME-SAGD in comparison to SAGD. The experiment was conducted in a cylindrical pressure vessel with a diameter of 0.425 m and a length of 1.22 m, which contained a sand pack with a porosity of 0.34 and a permeability of 5.0 D. The DME-SAGD experiment used a DME concentration of 10 mol% and a steam co-injection rate of 27.6 cm3/min [cold-water equivalent (CWE)] at 3000 kPa. Temperature distributions within the sand pack, along with injection and production histories, were recorded during the experiment. Subsequently, numerical simulations were performed to history-match the experimental data, and the calibrated simulation model was used to analyze details of compositional flow characteristics. Results showed that the 10 mol% DME-SAGD experiment yielded a recovery factor of 92.7% in 4.2 days, and the SAGD experiment yielded a recovery factor of 68.6% in 6.0 days, for both of which the first 2 days were the preheating and the steam-only injection (SAGD) stages. The peak rate of bitumen production was 43.8 mL/min in the DME-SAGD experiment, which was more than twice greater than the peak rates observed in the SAGD experiment. The substantially increased rate of bitumen production resulted in a cumulative steam-to-oil ratio in DME-SAGD that was less than half of that in SAGD. Analysis of experimental results indicated that the solubility of DME in the aqueous and oleic phases caused different flow characteristics between DME-SAGD and SAGD. For example, the oleic and aqueous phases were more uniformly distributed in the sand pack in the former. Simulations indicated that DME-SAGD had a uniform distribution of greater grid-scale Bond numbers and increased oleic-phase mobilities in comparison to SAGD.
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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".