Nanoparticle Assisted Foam Stability Under SAGD Conditions
Bibliographic record
Abstract
Abstract Thermal oil recovery processes, and more specifically steam assisted gravity drainage (SAGD), is one of the two commercial methods to produce heavy oil. In the later stages of SAGD heat losses increase. One solution to improve heat losses in the steam chamber is to co-inject a foaming solution with non-condensable gases. It is expected that such a scheme will redirect steam towards heating oil and not the overburden. An appropriate foaming agent is required for successful implementation of a steam-foam process. Conventional laboratory techniques have provided some indication of foam stability with different types of surfactants but failed to match the reservoir conditions and time scale. Recently, the use of nanoparticles along with surfactants has gained attention as a method to stabilize foams under thermal operating conditions. The aim of this research is to investigate the thermal stability of foam under steam conditions (temperatures around 200 °C) using mixtures of different surfactants and silica nanoparticles. A series of foam stability tests were conducted at temperature ranges of 170 °C to 212 °C and pressures of 2.78 MPag and 4.22 MPag using two different anionic surfactants and four different bare and coated silica nanoparticles. The foamy solutions were prepared with a combination of different surfactants and nanoparticles, which were co-injected with N2 gas into a sand pack to generate foam at different temperatures and pressures. The generated foam was then transferred to a high pressure and high temperature visual cell and the foam half-life was measured as the indicator of its decay. It was observed that a small deviation from the dew point (decreasing the temperature or increasing the pressure) significantly improved foam stability. Addition of nanoparticles proved to be synergistic as the foam half-life near the steam dew point increased about four-fold compared to surfactant only foams. Among the tested nanoparticles, the use of polyethylene glycol (PEG) coated silica nanoparticles along with an anionic surfactant resulted in the highest foam stability near the steam dew point. To date, most of the foam stability tests have been conducted at temperatures below 200 °C with the focus on using surfactants. This research extended the foam stability tests to temperatures in excess of 200°C using mixtures of surfactants and nanoparticles. Although the foam stability still needs to be improved for reservoir-scale application, our screening methodology presents a realistic process of generating foam in a porous medium with nanoparticles and surfactants under a desired thermodynamic state for subsequent foam thermal stability testing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".