Investigation of Low Pressure ES-SAGD
Bibliographic record
Abstract
Abstract SAGD is a commercially proven technology for Athabasca reservoirs. It produces high oil rates and high ultimate recoveries. Injecting a solvent with the steam can reduce required steam rates and/or improve oil rates and recovery. Oil viscosity is reduced both thermally and by solvent dissolution. A scaled model experiment was performed at the Alberta Research Council to examine the effectiveness of the Expanding Solvent-SAGD (ES-SAGD) process at a low pressure (1,500 kPa), which was still high enough to allow sufficient drive to transport the produced fluids to the surface at typical Athabasca formation depths. A lower SAGD pressure typically results in reduced oil production as a result of the correspondingly lower steam temperature. However, a lower operating pressure can also result in a reduced SOR because of lower steam density (lower mass of steam required to fill a specific volume of the steam chamber) and also a higher heat of vaporization, which allows more heat to be transferred to the reservoir when a given mass of steam is condensed. At lower pressures, the steam saturation temperature changes more quickly with saturation pressure than it does at higher pressures. Thus, at low pressure, a small reduction in pressure can lead to steam flashing in the producer and create unstable conditions. This experiment was history matched and then a parametric investigation was performed based on field scale numerical simulations using CMG STARS. The same solvent (gas condensate) and solvent concentration (9.3 volume%) were used in the lab and field scale simulations. The condensate had many components, which were represented in the numerical simulations by four pseudo-solvent components. 2-D and 3-D field scale simulations examined the effect of: operating pressure, injection rate, sub-cool, oil and gas phase diffusion and dispersion, live oil versus dead oil performance, use of pressure drawdown when oil rates have declined, and compared low pressure ES-SAGD to low pressure SAGD. The simulations indicated that the effects of production pressure, sub-cool, and solvent concentration must be considered simultaneously as they impact each other. At 1,500 kPaa production pressure and 10 °C sub-cool, co-injection of solvent with steam increased the average oil rate by 15% while reducing the SOR as compared to SAGD at the same operating pressure.
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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) | 0.002 | 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".