Testing and History Matching ES-SAGD (Using Hexane)
Bibliographic record
Abstract
Abstract For the first time, we present in public domain, experimental and history-matched results of 2D scaled laboratory testing of ES-SAGD with hexane as the co-injected solvent. Experimental results of ES-SAGD (with hexane) were also compared with the equivalent SAGD. The 2 experiments, which were conducted at the Alberta Research Council's Thermal Gravity laboratory, are 2-D high pressure/high temperature experiments and were conducted at 2100 kPag +/− 50 kPag. The comparison of ES-SAGD and SAGD experiments shows that ES-SAGD using hexane performed better than an equivalent SAGD experiment. The energy consumption per unit oil recovered for ES-SAGD was lower than that of SAGD (11.5% less). The average oil recovery within the first 500 minutes (i.e. 11.3 years at field-scale) for the ES-SAGD process was also much higher (~10.93% higher). The ES-SAGD result was history-matched with a commercial reservoir simulator (CMG STARS). The history-matched ES-SAGD experiment gave satisfactory results in terms of oil production rates, cumulative oil production, and temperature distributions. The results presented in the paper provide data that can be scaled to field and assist in the design, optimization and parameter selection when ES-SAGD (with hexane or a pseudo-hexane solvents mixture) is considered as a recovery technology. Multi-pattern simulations based on history-matched data of a single well pair (pattern) show that multi-well pair (pattern) simulations can provide a suitable approximate analog for multi-pattern experiments, which can be scaled and used for planning and optimizing a pilot or a small-scale commercial project.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".