Kinetics of the In-Situ Upgrading of Heavy Oil by Nickel Nanoparticle Catalysts and Its Effect on Cyclic-Steam-Stimulation Recovery Factor
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it