Mechanics of SAGD Efficiency Improvement Using Combination of Chemicals: An Experimental Analysis through 2D Visual Models
Bibliographic record
Abstract
Summary Steam-assisted gravity drainage (SAGD) is (and will be) a dominating method for in-situ recovery of heavy oil and bitumen in Canada. Its efficiency, however, has been a chronic problem due to the excess use of water and energy causing inflated costs. Solutions are needed to both maintain the production, especially at the late stages, and reduce the amount of steam. One method to improve efficiency is injection of chemicals with steam. This study addresses this problem by focusing on three critical aspects of SAGD: sweep improvement (faster and larger chamber growth), better displacement efficiency (lower residual oil), and reducing the amount of steam (lower steam/oil ratio and lowered steam temperature). By addressing these issues, we provide answers for the proper formulation of chemical blends, optimal injection strategies (continuous or slug), and the right time to introduce chemicals (beginning, midstream, or mature phase). The efficiency of a single chemical additive in recovery improvement is limited because it serves for only one of the mechanisms previously listed. For a better performance, blending chemicals with different functionalities was proposed in this study. Additives showing the highest microscopic oil displacement efficiency (heptane, Novelfroth® 190) and the highest areal sweep efficiency EA [LTS-18, silicon dioxide (SiO2) nanoparticle, Tween™ 80, deep eutectic solvent (DES) 11] in our previous studies were selected. Eleven different combinations of these six chemical additives were tested for different injection strategies, and the SAGD process was visualized on Hele-Shaw cells filled with heavy oil. The interaction between different chemical additives was determined by analyzing emulsification, wettability alteration, and the growth and shape of the steam chamber. The optimal formulation and ideal injection strategy were selected by analyzing ultimate EA, microscopic oil displacement efficiency, ultimate oil recovery, energy and water consumption, and the price of chemical additives. The interaction rule of combined chemical additives and their contribution to the recovery during SAGD were clarified, and the optimal injection strategies (best blends and proper time to introduce chemicals) were identified to provide a reference for chemical selection for field applications. This is a critical attempt to reduce the steam/oil ratio (particularly the amount of steam used) in SAGD applications, especially at mature stages.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".