Injectivity, Potential Wettability Alteration, and Mineral Dissolution in Low-Salinity Waterflood Applications: The Role of Salinity, Surfactants, Polymers, Nanomaterials, and Mineral Dissolution
Bibliographic record
Abstract
Waterflooding, a key method for secondary hydrocarbon recovery, has been employed since the early 20th century. Over time, the role of water chemistry and ions in recovery has been studied extensively. Low-salinity water (LSW) injection, a common technique since the 1930s, improves oil recovery by altering the wettability of reservoir rocks and reducing residual oil saturation. Recent developments emphasize the integration of LSW with various recovery methods such as CO2 injections, surfactants, alkali, polymers, and nanoparticles (NPs). This article offers a comprehensive perspective on how LSW injection is combined with these enhanced oil recovery (EOR) techniques, with a focus on improving oil displacement and recovery efficiency. Surfactants enhance the effectiveness of LSW by lowering interfacial tension (IFT) and improving wettability, while ASP flooding helps reduce surfactant loss and promotes in situ soap formation. Polymer injections boost oil recovery by increasing fluid viscosity and improving sweep efficiency. Nevertheless, challenges such as fine migration and unstable flow persist, requiring additional optimization. The combination of LSW with nanoparticles has shown potential in modifying wettability, adjusting viscosity, and stabilizing emulsions through careful concentration management to prevent or reduce formation damage. Finally, building on discussions around the underlying mechanisms involved in improved oil recovery and the challenges associated with each approach, this article highlights their prospects for future research and field implementation. By combining LSW with advanced EOR techniques, the oil industry can improve recovery efficiency while addressing both environmental and operational challenges.
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How this classification was reachedexpand
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 | 0.000 | 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 teacher head, 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".