Integrating Silicate-Based Nanoparticles with Low-Salinity Water Flooding for Enhanced Oil Recovery in Sandstone Reservoirs
Bibliographic record
Abstract
A large number of researchers have endeavored to delineate the effects of injecting brine with a low ionic strength in oil reservoirs in the past decade. However, we still cannot conclude the overriding mechanism(s) of recovering oil from this technique. Even with a detailed review of the literature, the effect of low-salinity water flooding (LSWF) shows that a bewildering array of conflicting results have been reported. From the physicochemical point of view, understanding how brine and oil chemistry affects oil recovery helps to optimize recovery from such processes. Furthermore, the use of brine with low ionic strength coupled with nanoparticles during enhanced oil recovery (EOR), especially in the presence of monovalent ions and/or divalent cations, presents a new field of study that requires further investigations. Herein, the main objective of this study was to investigate the fluid/rock interactions at different salinities in the presence of various surface-modified pyroxene nanoparticles. Pyroxene was surface modified using polyethylene amine (PEI), poly(ethylene oxide) (PEO), and triethoxyoctylsilane (TOS). Surface charge, wettability measurements in the presence of various ions in the irreducible water, and core flooding experiments have been conducted to understand the underlying mechanism(s). The surface charge was evaluated by zeta potential measurements, and wettability was determined by the contact angle, imbibition, and relative permeability measurements. Sandstone outcrops and three oil samples with different composition were used. The results show that adding 0.005 wt % nanoparticles to brine with low ionic strength (1000 ppm) can improve the nanofluid stability and EOR. Additionally, in the presence of LSWF combined with nanoparticles, the thickness of the double layer on the rock surface greatly expands, thus increasing the magnitude of zeta potential compared to LSWF alone. The contact angle in the presence of LSW alone and N-PEO, N-PEI, and N-TOS nanofluids was measured as 94 ± 3, 118 ± 3, 112 ± 3, and 130° ± 3°, respectively, conforming wettability alteration from oil/neutral wet to stronger water-wet. Moreover, the greater repulsive force due to double-layer expansion creates a significant shift in the relative permeability curve to the right. Consequently, this results in improved oil recovery by about 15% of the oil originally in place. Based on the obtained findings, LSWF coupled with nanoparticles provides a prospect of being applied in EOR.
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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) | 0.000 | 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 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".