Effects of Nanoparticles on Gas Production, Viscosity Reduction, and Foam Formation during Nanofluid Alternating Gas Injection in Low and High Permeable Carbonate Reservoirs
Bibliographic record
Abstract
Abstract Some carbonate reservoirs are low‐permeable and mixed‐wet to oil‐wet. Application of enhanced recovery methods to decrease residual oil saturation is the main challenge in such reservoirs. Water‐flooding is not an appropriate method to improve the recovery due to the wettability of carbonate reservoirs. Water alternating gas (WAG) injection is used as another method but it faces some problems. To enhance the performance of the WAG method, the wettability of rock should be changed and mobility of the injected gas should be controlled. In our previous study (Moradi et al.[32]), to improve the WAG‐EOR process and overcome the problems, we developed the nanofluid‐alternating gas (NWAG) approach by adding nanoparticles to the aqueous phase. Our study indicated that the NWAG process could improve oil recovery in comparison to the conventional WAG method in different core samples with different lithology and permeability. In this study, core‐flooding experiments, dynamic foam generation, and viscosity measurement were performed to investigate the effect of nanoparticles on gas production, viscosity reduction, and possibility of foam formation in the NWAG process. The results indicated that the gas production was decreased in the NWAG process. In addition, viscosity of produced oil was lower in the NWAG process; CO2 dissolved in the oil and led to oil swelling and oil viscosity reduction. Oil recovery was higher in the samples with lower permeability due to the formation of foam in the porous media. Foam formation increased viscosity and controlled mobility, which led to better sweep efficiency.
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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".