Laboratory Testing of Addition of Solvents to Steam to Improve SAGD Process
Bibliographic record
Abstract
Abstract Steam Assisted gravity drainage (SAGD) is demonstrated as a proven technology to unlock heavy oil and bitumen in Canadian reservoirs. One of the long-term concerns with the SAGD process is high energy intensity and related environmental impacts. The addition of suitable hydrocarbon solvents to steam has long been regarded as the simplest and most effective method to increase SAGD performance. Higher oil recovery, accelerated oil production rate, reduced steam to oil ratio and generally more favorable economics is expected from the addition of potential hydrocarbon additives to steam. This paper summarizes experimental results of addition of potential solvents to steam in SAGD process. N-Hexane and n-heptane were co-injected with the steam and the experimental results were compared with pure steam injection. In addition, pure heated n-hexane was injected in one experiment to assess the performance of solvent-based processes. Experiments were conducted using a scaled two-dimensional physical model. Peace River Bitumen samples were used to conduct the experiments at 80 psia. Experimental results were analyzed to determine the key variables involved in Solvent Assisted SAGD (SA-SAGD) processes. Solvent choice is not solely dependent on mobility improvement capability but also reservoir properties and operational conditions. Co-injection of suitable solvents with the steam led to accelerated oil production rate, higher oil recovery and lower energy to oil ratio. Solvent requirement for pure heated n-hexane injection was considerably high. The vaporized solvent chamber expansion was slow due to low heat content of the solvent and heat losses.
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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) | 0.000 | 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".