Synergistic Effect between Surfactant and Nanoparticles on the Stability of Foam in EOR Processes
Bibliographic record
Abstract
Summary The major challenge in enhanced oil recovery (EOR) by gas injection is poor volumetric sweep efficiency, mainly due to the high gas mobility and reservoir heterogeneity. Injecting gas as a foam increases sweep efficiency, but maintaining foam stability within the reservoir remains a challenge. This research evaluates the synergistic interaction of one type of nanoparticle and a surfactant to increase foam stability, using the concentration ratio of the two components to tune the affinity of the nanoparticle for the gas/liquid interface. We test the capability of the synergistic two-component system to stabilize methane foam and compare it with foam stabilized by surfactants only. A key distinction is the foam stability upon contact with oil, and we explain the observations in static and dynamic conditions. Foam stability was measured in both static (bubble structure) and dynamic (flow through porous media) conditions. In the static test, foam is generated by the shaking method, and foam texture (bubble size and shape) and the decay of foam height with time are indicators of foam stability. To test static stability in the presence of oil, heavy oil is injected into the foam/liquid interface. In dynamic tests, foam is pregenerated before flowing at elevated pressures into sandpacks containing various oil saturations. Normalized pressure gradient and apparent viscosity are the indicators of foam stability and effectiveness for improving oil recovery. The extent to which nanoparticles are covered with surfactant governs the foam stability, in both static and dynamic conditions. Static foam is stable in the presence of oil only if the nanoparticles are partially covered by the surfactant. In the dynamic test, foam stabilized with only the surfactant collapses in the porous media when oil is present. Nanoparticles alone could not generate foam regardless of the presence of oil or salinity, but foam stabilized with nanoparticles partially covered by surfactant is stable in the presence of both residual and initial oil, and foam apparent viscosity could reach up to 400 cp at residual heavy oil condition. In both static and dynamic conditions, nanoparticles completely covered with a bilayer of surfactant do not stabilize foam in the presence of oil. Partially covered nanoparticle foam also demonstrated salt tolerance in both static and dynamic tests, and foam apparent viscosity can reach up to 200 cp with high salinity and residual heavy oil presented. Thus, at appropriate surface coverage, the combination of nanoparticles and surfactant is more effective than either stabilizer alone. The result shows that interaction of surfactant and nanoparticles is important in foam stability in the porous media with oil. In particular, this interaction is synergistic at certain coverage. This type of synergy can provide much more robust mobility control for EOR processes involving gas injection.
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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 | 0.001 |
| 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) | 0.000 | 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".