A Novel Thermo-Salinity-Responsive Nanographite System for Enhanced Oil Recovery in Deep Reservoirs
Why this work is in the frame
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Bibliographic record
Abstract
In deep oil reservoir development, enhanced oil recovery (EOR) techniques encounter significant challenges under high-temperature and high-salinity conditions. Traditional profile-control agents often fail to maintain stable blocking under extreme conditions and exhibit poor resistance to high temperature and high salinity. This study develops a functionalized nanographite system (the MEGO system) with superior high-temperature dispersibility and thermosalinity-responsive capability through polyether amine (PEA) grafting and noncovalent interactions with disodium naphthalene sulfonate (DNS) molecules. The grafted PEA and DNS provide steric hindrance and electrostatic repulsion, enhancing thermal and salinity resistance. After ten days of aggregation, the MEGO system forms stable particle aggregates (55.51–61.80 µm) that are suitable for deep reservoir migration and profile control. Both experiments and simulations reveal that particle size variations are synergistically controlled by temperature and salt ions (Na + , Ca 2+ , and Mg 2+ ). Compared with monovalent ions, divalent ions promote nanographite aggregation more strongly through double-layer compression and bridging effects. In core displacement experiments, the MEGO system demonstrated superior performance in reservoirs with permeabilities ranging from 21.6 to 103 mD. The aggregates formed within the pore throats significantly enhanced flow resistance, expanded the sweep volume, and increased the overall oil recovery to 56.01%. This research indicates that the MEGO system holds excellent potential for EOR in deep oil reservoirs.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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