Steam Additives to Reduce the Steam-Oil Ratio in SAGD: Experimental Analysis, Pilot Design, and Field Application
Bibliographic record
Abstract
Abstract Producing bitumen using SAGD requires a significant amount of water and energy, resulting in a large amount of greenhouse gas emissions. Therefore, reducing the steam-oil ratio (SOR) in SAGD is critical to make the oil recovery process profitable and sustainable in a carbon-constrained world. This paper presents the potential benefits of co-injecting water-soluble volatile additives with steam in SAGD. The objective of the process is to decrease the SOR while maintaining SAGD-like oil production rates at economical chemical additives concentrations. Through a comprehensive experimental study, multiphase behaviour of the additive-water-bitumen system, mixture's viscosity, additive thermal stability, adsorption, emulsion stability, and recovery performance were evaluated. Extensive coreflooding experimental tests quantified the potential for improved oil recovery and SOR reduction. The experimental variables included additive concentration, water-oil ratio, and temperature. The studies showed that the additives improved oil recovery by promoting the formation of oil-in-water emulsions at the producing SOR. A series of reservoir simulation studies were also conducted for a field pilot design and evaluation of key performance indicators. Two different methodologies, equilibrium and non-equilibrium, were used to model the steam additive behaviour under both transient and steady-state conditions. Data obtained from coreflooding and viscosity measurements were the primary inputs of the reservoir simulation models. The fine-tuned reservoir simulation model quantified the technology uncertainties using multiple equally probable realizations of the reservoir to design and optimize the field pilot's injection scenarios and operating conditions. The simulation results showed SOR reduction of up to 25% with steam additives co-injection for the designed concentrations. Different phenomena such as additive transportation, condensation, additive degradation, and adsorption in a growing steam chamber were included in the numerical model. Based on the experimental and reservoir simulation results, a 4-well pair field pilot was designed, built, and put in operation at the Surmont SAGD project.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".