Kinetics of the In-Situ Upgrading of Heavy Oil by Nickel Nanoparticle Catalysts and Its Effect on Cyclic-Steam-Stimulation Recovery Factor
Bibliographic record
Abstract
Summary The effect of nickel nanoparticles on in-situ upgrading of heavy oil (HO) during aquathermolysis and the effect of this process on the recovery through cyclic steam injection were studied. High-temperature experiments were conducted with a benchtop reactor to study the kinetics of the reactions among oil, water, and sandstones in the presence and absence of the nickel nanoparticles. Eighteen experiments were conducted at three different temperatures and at three different lengths of time, and the evolved hydrogen sulfide during the reaction was analyzed. The kinetic analysis showed that nickel nanoparticles reduce the activation energy of the reactions corresponding to the generation of hydrogen sulfide by approximately 50%. This reaction was the breakage of C-S bonds in the organosulfur compounds of the HO. The maximal catalysis effect was observed to be at a temperature of approximately 270°C. Also, the simulated-distillation gas-chromatography (GC) analysis of the oil sample, after the aquathermolysis reactions, confirmed the catalysis effect of nickel nanoparticles. According to this analysis, by catalytic process, the concentration of the components lighter than C30 increased whereas the concentration of heavier components decreased. Next, the effect of the catalytic aquathermolysis on the recovery factor of the steam-stimulation technique was studied. The stimulation experiments consisted of three injection/soaking/production phases. The results showed that the nickel nanoparticles increased the recovery factor by approximately 22% when the nanoparticles were injected with a cationic surfactant and xanthan-gum polymer. This increase of recovery was approximately 7% more than that of the experiment conducted with the surfactant and polymer only.
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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".