Transportation and Interaction of Nano and Micro Size Metal Particles Injected to Improve Thermal Recovery of Heavy-Oil
Bibliographic record
Abstract
Abstract Studies on the application of transition metal particles for heavy oil or bitumen up-grading were conducted in the absence of a porous medium, mainly measuring the characteristics of heavy-oil in reaction with metal ions at surface conditions. Dynamic tests on porous media are needed to clarify the injection possibility of the metal particles and their effect on in-situ recovery and up-grading heavy oil. Injection of metal particles may deteriorate the recovery process by damaging porous media due to attractive forces such as van der Waals and electrostatic forces between particles and porous rock. A better understanding of these forces and their importance in the retention of particles is required. In this paper, the injectivity and transport of nickel particles was studied. The injected suspension was stabilized using Xanthan gum polymer and ultrasonication. The effect of the solution pH, which controls the magnitude of the repulsive electrostatic forces, was clarified. Stabilization of the metal particles suspension was studied at different pH values through zeta potential measurements. Also, the zeta potential of the recovered suspensions was studied to confirm the stability of the suspension during travel through the porous medium. Depending on the size and type, particles carry different charges. Therefore, the stabilization pH and dispersant concentration was different from one sample to another. The results of the injectivity tests confirmed the lower retention of nanoparticles in comparison with micron-sized particles. A steam simulation process was applied in the presence and absence of metal particles, and heavy-oil recoveries were monitored. Higher recovery was achieved when nickel nanoparticles were used. The changes in asphaltene content and the viscosity of the heavy oil confirm the catalytic effect of the nickel nanoparticles on the in-situ upgrading of heavy-oil.
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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".