Investigation of the effect of surfactant co-injection on steam chamber expansion and oil production in steam-assisted gravity drainage in reservoirs with barrier layers
Bibliographic record
Abstract
The use of surfactants as additives to enhance the performance of steam-assisted gravity drainage (SAGD) has been a promising concept for decades. While previous studies largely focused on homogeneous reservoirs, this study has investigated the impact of surfactants on steam chamber expansion, oil production, and recovery in SAGD applied to reservoirs with separated barrier layers of varying permeability, thickness, and configurations. A comparative analysis of SAGD and surfactant-aided SAGD (SA-SAGD) was conducted through experimental and numerical simulations using a 3D model based on the Long Lake Reservoir in Canada. The results showed that surfactant co-injection mitigated the negative impact of low-permeability layers on steam chamber growth, production rates, and oil recovery. Unlike in conventional SAGD, the steam chamber was able to penetrate through barrier layers thicker than 4 cm (equivalent to 4 m in the field) and with a permeability of 150 mD resulting in more than 50% higher oil recovery and a near two-fold increase in recovery rates when surfactant was used. Oil production rates were also 1.5 times higher than those achieved with SAGD alone. This study provides new insights into overcoming the limitations posed by barrier layers, offering potential improvements for SAGD operations in complex reservoir conditions.
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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) | 0.000 | 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 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".