Low-Salinity Brine Enhances Oil Production in Liquids-Rich Shale Formations
Bibliographic record
Abstract
Abstract Low-salinity waterflooding has proven to be an appealing technique for enhancing oil recovery in conventional reservoirs. However, few studies have been conducted on low-salinity brines (LSBs) for hydraulic fracturing in liquids-rich shale plays with or without surfactant. Additionally, as operators tend to shift from fresh water to 100% produced water, the implications of such a switch must be understood from a production standpoint. Therefore, the effects of LSBs on oil recovery from liquids-rich shale should be investigated. In this study, LSBs with or without surfactant were injected into the crushed, oil saturated Muskwa shale from Canada. Laboratory results suggest that LSB (≤4% KCl) extracts more hydrocarbon than high salinity brine (HSB) (≥8% KCl). Notably, additional oil recovery was observed when surfactant was used in LSB. Interfacial tension (IFT) reduction decreased with increasing salinity but remained constant for LSB with surfactants across all salinities examined. Short-lived oil in water emulsions were observed in LSB in the presence of surfactant. Additionally, LSBs with surfactant were injected into a microfluidic based reservoir on a chip (ROC) device, where pore size was comparable to that of shale. The visualized oil recovery on the ROC was consistent with that found in core flooding tests. These reported results provide a potential methodology for optimizing source water before hydraulic fracturing operations. LSBs with properly tailored surfactant additives are imperative to helping enhance well productivity.
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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".