Characterization of Reservoir Heterogeneity for SAGD and ES-SAGD: Under What Type of Heterogeneity is ES-SAGD More Likely to Lower SOR?
Bibliographic record
Abstract
Abstract Expanding-solvent steam-assisted gravity drainage (ES-SAGD) is a potential method to reduce steam-oil ratio (SOR) of SAGD, which is a critical concern especially for highly-heterogeneous reservoirs. The main objective of this research is to investigate the flow characteristics of heterogeneous reservoirs in which solvent is more likely to lower SOR of SAGD. SAGD and ES-SAGD with normal hexane are simulated for fifty geostatistical realizations consisting of clean sand and shale, qualitatively representative of the middle member of the McMurray formation. Thermodynamic models are calibrated with experimental phase behavior data for reliable comparison between SAGD and ES-SAGD, including the water solubility in oil at elevated temperatures. Results show that the SOR reduction by steam-solvent coinjection is positively correlated with the increase in SAGD's SOR due to heterogeneity. Enhancement of bitumen flow by dilution is more important for lowering SOR for those reservoirs in which the permeability variation makes slow-flow regions during SAGD. The key observation of simulation results from a statistical standpoint is that a larger amount of bitumen tends to be diluted by solvent in those reservoirs for which SAGD exhibits slow production of bitumen. Then, the observed results are analyzed by use of SAGD analytical equations that clarify several influential factors for bitumen flow beyond the edge of a steam chamber. It is shown that dilution of bitumen by solvent in steam-solvent coinjection becomes more significant where flow barriers limit the local bitumen flow under SAGD even at high temperatures. In such slow-flow regions, the bitumen flow rate can be substantially increased by accumulation of solvent in ES-SAGD, which reduces the oleic-phase viscosity and increases the oleic-phase saturation and, therefore, relative permeability. Solvent accumulation within a steam chamber can also reduce thermal losses because of lower operating-chamber temperatures.
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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 | 0.000 |
| 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".