A Comparative Simulation Study of Addition of Solvents to Steam in 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. Addition of potential alkane solvents to steam in processes such as ES-SAGD can reduce the high use of energy and green-house emissions in SAGD. However, the principal challenge is the high cost of the solvents. As a result, the economic viability of solvent assisted processes highly depends on the original reservoir and fluid properties and the operating strategy used to co-inject the solvents. The main objective of this study is to compare the simulation results of addition of potential solvents to steam in two different types of reservoirs, cold lake and Athabasca. Propane, Butane, Pentane, Hexanes and Heptanes with different proportions from 1%-20% by weight have been co-injected with the steam. The simulations carried out in absence and presence of initial solution gas to find out the effect of solution gas on performance of SAGD and solvent assisted SAGD processes. Simulation results show that initial solution gas reduces the oil recovery especially in Athabasca reservoir. A varying thickness non-condensable gas layer impedes heat transfer from the condensing steam to bitumen zone. Hydrocarbon additives create a high oil phase mobility zone resulting in production acceleration. Solvents heavier than butane are considered suitable candidates for Athabasca type and butane gave better results in Cold Lake type reservoir under operating conditions of this study. In addition, a detailed study is carried out on the properties of different phases such as phase mobility, saturation and viscosity at the steam solvent-oil interface to have a better understanding of the effect of presence hydrocarbon additives in the steam chamber.
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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 | 0.000 |
| 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 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".