Influence of different nanoparticles on the gas injection performance in <scp>EOR</scp> operation: Parametric and <scp>CFD</scp> simulation study
Bibliographic record
Abstract
Abstract This study aims to simulate the process of enhanced oil recovery (EOR) during gas injection along with nanoparticles and investigate the affecting parameters in a conventional carbonate oil reservoir. Ansys Fluent software with a suitable multiphase model was used to simulate natural gas injection with a nanoparticle into a core sample. The simulation model was validated with a laboratory test of natural gas injection. Then, to obtain the optimal values of each of the parameters affecting the process of EOR during the natural gas injection along with nanoparticles, the design of the experiment was carried out with the help of Qualitek‐4 software and the Taguchi method. Therefore, three factors, including nanoparticle type (clay, titanium oxide, and silica nanoparticles), nanoparticle diameter (2–50 nm), and the volume fraction of nanoparticles in the base fluid (0.5–5 vol.%), as influential factors on the EOR during natural gas injection along with nanoparticles were chosen. The results of the numerical study indicated that silica nanoparticles significantly affect EOR more than clay and titanium oxide nanoparticles. Moreover, the smaller the diameter of nanoparticles (close to 2 nm) and the more significant the volume fraction of nanoparticles in the base fluid (close to 5 vol.%), the higher the oil recovery factor will be. This phenomenon occurs due to changes in the density and viscosity of the base fluid and, consequently, improves the mobility ratio of the injected fluid. On the other hand, the tiny size of nanoparticles allows them to easily enter the pores of the reservoir rock without entrapping and producing oil from them. Eventually, the highest oil recovery factor (59%) was obtained using silica nanoparticles with a diameter of 2 nm and a volume fraction of 5 vol.% in natural gas injection.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".