Steam-Oil Ratio in Steam-Solvent Coinjection Simulation for Homogeneous and Heterogeneous Bitumen Reservoirs
Bibliographic record
Abstract
This research presents a mechanistic analysis of expanding-solvent steam-assisted gravity drainage (ES-SAGD) for heterogeneous reservoirs in terms of cumulative steam-oil ratio (SOR) as a function of cumulative bitumen production. Simulation case studies for SAGD and ES-SAGD with normal hexane at 35 bars are conducted for geostatistical realizations of two types of heterogeneous Athabasca-bitumen reservoirs. For the first type, low-permeability mudstone barriers are oriented horizontally. For the second type, they are inclined and more representative of the middle McMurray member. The solubility of water in the oleic phase at elevated temperatures is properly modeled to ensure reliable comparison between steam-assisted gravity drainage (SAGD) and ES-SAGD. Simulation results show that ES-SAGD is less sensitive to heterogeneity than SAGD in terms of cumulative SOR. On average, the reduction in SOR due to steam-solvent coinjection is simulated to be greater under heterogeneity. The reduction in SOR is greater for reservoir models with inclined mudstone barriers than in those with horizontal mudstone barriers. Analysis of simulation results indicates that the injected solvent tends to accumulate more significantly under heterogeneity, which enhances the mechanisms of ES-SAGD, such as dilution of bitumen by solvent and reduced thermal losses to the overburden. Tortuous hydraulic paths and slower gravity drainage under heterogeneity enhance the mixing between solvent and bitumen in the transverse direction along the edge of a steam chamber. Then, a larger amount of the accumulated solvent tends to facilitate lower temperatures near the chamber edge.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 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".