Catalytic-Effect Comparison Between Nickel and Iron Oxide Nanoparticles During Aquathermolysis-Aided Cyclic Steam Stimulation
Bibliographic record
Abstract
Summary Cyclic steam stimulation (CSS) is a proven effective technique for boosting oil production. Metal species can act as a catalyst for aquathermolysis reactions between heavy oil and water during the CSS process. For this paper, a series of CSS experiments with and without metal nanoparticles was conducted at temperatures up to 220°C to compare the performance of nickel and iron oxide nanoparticles in promoting aquathermolysis reactions in CSS; further, different loadings of metal nanoparticles were also tested in the CSS experiments. During the experiments, we monitored the variations of oil recovery factor, oil viscosity, gas composition, and water production. The experimental results show that both nickel and iron oxide nanoparticles can act as a catalyst for aquathermolysis reactions and reduce the viscosity of heavy oil. However, their respective catalytic effects differ significantly: nickel nanoparticles can break the C-S bond more effectively than iron oxide metal nanoparticles, thus achieving a higher ultimate oil recovery factor of CSS. The introduction of metal nanoparticles boosted oil production and increased water production from the very first cycle in the CSS process. The gas chromatography (GC) analysis and the pressure data recorded during each soaking period revealed that a higher amount of evolved gas including alkenes and hydrogen sulfide was generated in the early stage, increasing reservoir pressure and forcing more condensed water to be produced from the sandpack.
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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 | 0.001 | 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 | 0.001 |
| 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".