Experimental Evaluation of Residual Oil Saturation in Solvent-Assisted SAGD Using Single-Component Solvents
Bibliographic record
Abstract
The massive heavy oil reserves in the Athabasca region of northern Alberta depend on steam-assisted gravity drainage (SAGD) for their economic exploitation. Even though SAGD has been successful in highly viscous oil recovery, it is still a costly technology because of the large energy input requirement. Large water and natural gas quantities needed for steam generation imply sizable greenhouse gas (GHG) emissions and extensive post-production water treatment. Several methods to make SAGD more energy-efficient and environmentally sustainable have been attempted. Their main goal is to reduce steam consumption whilst maintaining favourable oil production rates and ultimate oil recovery. Oil saturation within the steam chamber plays a critical role in determining both the economic viability and resource efficiency of SAGD operations. However, accurately quantifying the residual oil saturation left behind by SAGD remains a challenge. In this experimental research, sand pack Expanding Solvent SAGD (ES-SAGD) coinjection experiments are reported in which Pentane -C5H12, and Hexane -C6H14 were utilised as an additive to steam to produce Long Lake bitumen. Each solvent is assessed at three different constant concentrations through time using experiments simulating SAGD to quantify their impact. The benefits of single-component solvent coinjection gradually diminish as the SAGD process approaches its later stages. ES-SAGD pentane coinjection offers a smaller improvement in recovery factor (RF) (4% approx.) compared to hexane (8% approx.). Between these two single-component solvents, 15 vol% hexane offered the fastest recovery. The obtained data in this research provided compelling evidence that the coinjection of solvent under carefully controlled operating conditions, reduced overall steam requirement, energy consumption, and residual oil saturation allowing proper adjustment of oil and water relative permeability curve endpoints for field pilot reservoir simulations.
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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) | 0.001 | 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".