Compositional Mechanisms in SAGD and ES-SAGD With Consideration of Water Solubility in Oil
Bibliographic record
Abstract
Abstract Experimental data have shown that the solubility of water in the oleic (L) phase (xwL) can be significant at elevated temperatures. However, xwL was not properly considered in prior studies of SAGD and ES-SAGD. The main objective of this research is to present a detailed study of compositional mechanisms in SAGD and ES-SAGD simulation by considering xwL. The importance of properly considering xwL is shown in economic evaluation of these bitumen-recovery processes. The phase-behavior models used in this research are carefully created on the basis of experimental studies presented in the literature. Mechanistic simulation studies are then conducted for SAGD and ES-SAGD. Co-injectants used in ES-SAGD simulations range from propane through n-decane. Results show that xwL enhances bitumen production in both SAGD and ES-SAGD mainly because xwL results in reduction of L-phase viscosity. The enhancement is more significant when the chamber-edge temperature is higher, because xwL increases with temperature. The enhancement of bitumen production observed in the case studies is 7.66% for SAGD, 4.08% for C6-SAGD, and 4.85% for C8-SAGD for a fixed period of operation at 35 bars. It is important to consider xwL in SAGD and ES-SAGD simulations, because the relative performance of ES-SAGD to SAGD tends to be overestimated without considering xwL. On the basis of a sensitivity analysis for economic performance of ES-SAGD, a guideline is presented to leverage xwL to improve bitumen production in ES-SAGD. As discussed in our prior research, solvent becomes effective in diluting bitumen and reducing steam requirement, only when it sufficiently accumulates near the chamber edge. New results show that water can act as a diluting agent until solvent sufficiently accumulates 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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".