Improvement of the SAGD Process by Use of Steam-Foam: Design and Assessment of a Pilot Through Reservoir Simulation
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Bibliographic record
Abstract
Abstract Although SAGD is a very popular in-situ extraction method in Canada, this thermal process relies on huge energy and water consumption to generate the steam. Irregular growth of the steam-chamber due to heterogeneities further degrades its yield. Contact between the steam chamber and the overburden also leads to heat losses. The objective of this paper is to investigate how Foam Assisted-SAGD could mitigate these technical issues and improve the efficiency of the SAGD process. Compositional thermal reservoir simulations are used to simulate and analyze a Foam Assisted-SAGD pilot. The shear-thinning effect close to the wells is also accounted for. The simulations are run on a homogeneous model mimicking the Foster Creek project in Alberta, Canada. Several type of injection sequences have been analyzed in terms of foam formation, back-produced surfactants and cumulative Steam-Oil-Ratio. Results are compared with the original SAGD performance. In order to propagate the foaming surfactants throughout the steam chamber the injection sequence needs to be properly determined. A simple continuous Foam Assisted-SAGD injection would lead to an accumulation of surfactant between the wells due to gravity segregation, preventing the foam from acting on the upper part of the steam chamber. Furthermore surfactant production occurs after a few weeks due to the proximity of the producer and the injector. A proper injection strategy of the type SAGD/slug/SAGD/slug is found to delay the chemical breakthrough and increase the amount of surfactant retained in the reservoir while allowing the surfactant propagation throughout the steam chamber. After optimization the Foam Assisted-SAGD process appears to be technically promising.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 it