Low Salinity Hot Water Injection with Addition of Nanoparticles for Enhancing Heavy Oil Recovery under Reservoir Conditions
Bibliographic record
Abstract
Abstract In this study, a novel technique of low salinity hot water (LSHW) injection with addition of nanoparticles has been developed to examine the synergistic effects of thermal energy, low salinity water (LSW) flooding, and nanoparticles for enhancing heavy oil recovery, while optimizing the operating parameters for such a hybrid enhanced oil recovery (EOR) method. Experimentally, one-dimensional (1D) displacement experiments under different temperatures have been performed, while two types of nanoparticles (i.e., SiO2 and Al2O3) are respectively examined as the additive in the LSW. The performance of LSW injection with and without nanoparticles at various temperatures is evaluated, allowing optimization of the timing to initiate low salinity water injection. The corresponding initial oil saturation, production rate, water cut, and ultimate oil recovery, are continuously monitored and measured under various operating conditions. Compared to conventional water injection, the low salinity water injection is found to effectively improve heavy oil recovery as an EOR technique in the presence of nanoparticles. Also, the addition of nanoparticles into the LSHW can promote synergistic effect of thermal energy, wettability alteration, and reduction of interfacial tension (IFT), which improves water displacement efficiency and thus enhances oil recovery. It has been experimentally demonstrated that such LSHW injection with the addition of nanoparticles can be optimized to greatly improve oil recovery up to 40.2% in heavy oil reservoirs with low energy consumption.
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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".