Steam-Foam Technology as an Option to Improve Steam Drive Efficiency
Bibliographic record
Abstract
Abstract The steam-foam process is an Enhanced Oil Recovery (EOR) method which aims to improve the performance of a traditional steam drive by using a foaming surfactant. A Canadian thermal project under development in NW Alberta, which will use steam drive to recover extra heavy oil from the Bluesky Reservoir, is a good candidate for the application of steam-foam. A pilot test is planned to evaluate the benefits of the steam-foam process in this reservoir. The steam-foam process is based on the use of a surfactant which, when co-injected with steam into the formation, generates foam. A candidate surfactant for steam-foam should be able to generate stable foam at high temperature, have a good thermal stability, a low rate of adsorption on the rock, and good solubility in brine. An experimental plan was designed to screen for appropriate surfactants to use in the field. Bulk foam height tests at high temperature, thermal degradation tests and static adsorption tests with disaggregated rock were carried out to screen the best surfactant. Two candidate surfactants were chosen based on the results. A pilot test plan was also developed for a proof-of-concept test of the candidate surfactant in the field. The primary success criterion for the test is an increase in the Bottom Hole Pressure (BHP) of the injector well after the start of surfactant injection. Core-flooding tests are currently underway to confirm the performance of the candidate surfactant in the porous medium and determine the value of parameters required for the pilot design. The generation of strong foam in the formation should result in not only a BHP increase in the injector, but also improvement of the Steam to Oil Ratio (SOR) and ultimate recovery. The oil uplift response is dependent on the pattern geometry and geology of the reservoir and may not be observed immediately. However, the BHP increase will be immediately observed provided that strong foam has been generated near the wellbore, and this is the focus of the proof-of-concept test. A more extensive field test is planned for a later date to evaluate SOR improvement and recovery uplift.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".