In-Situ Upgrading of Heavy Oil/Bitumen During Steam Injection by Use of Metal Nanoparticles: A Study on In-Situ Catalysis and Catalyst Transportation
Bibliographic record
Abstract
Summary Studies on the application of transition-metal catalysts for heavy-oil or bitumen in-situ upgrading were conducted in the absence of a porous medium, mainly measuring the characteristics of heavy oil in reaction with metal ions at static conditions with the help of a magnetic stirrer. Metal species in ionic form are not soluble in oil phase. Therefore, metal particles, as inhomogeneous catalysts, are considered in this paper. Furthermore, dynamic tests in porous media are needed to clarify the injection possibility of the metal particles and their effect on in-situ upgrading of heavy oil. Injection of metal particles may deteriorate the recovery process by damaging porous media because of 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 catalysis effect of pure nanometer-sized nickel during steam-injection application was compared with that of an industrial catalyst such as micron-sized Raney nickel. The changes in the viscosity, refractive index, and asphaltene content were measured after each test to analyze the catalysis effects. Nickel nanoparticles showed a better catalysis compared with Raney nickel. The approximate optimum concentration of the catalysts was determined. Then, the catalysis effect of nickel nanoparticles was studied in the presence of sandpack as a porous medium. The results showed accelerated catalysis in presence of the sands. Also, nickel nanoparticles improved the oil recovery factor. The next phase of this paper studies the injectivity and transport of nickel particles. The injected suspension was stabilized by use of xanthan gum polymer and ultrasonication. The effect of 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, particles carry different charges and have different settling velocities. Therefore, the stabilization pH and dispersant concentration were different from one sample to another. The results of the injectivity tests confirmed the lower retention and better injectivity of nanoparticles in comparison with micron-sized particles.
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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 | 0.000 |
| 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) | 0.001 | 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".