Insights into Whether Low Salinity Brine Enhances Oil Production in Liquids-rich Shale Formations
Bibliographic record
Abstract
Abstract Low salinity water (LSW) flooding has been an attractive technique for enhancing oil recovery. Several LSW mechanisms have been proposed to account for the additional oil recovery, such as interfacial tension (IFT) reduction, wettability alteration, clay migration and microdispersion. However, none of the adequate mechanisms can consistently explain different cases because of the nature of heterogeneity in crude oil and formation rock. Because of a gradual shift from using fresh water to produced water, there is a need to investigate the effects of low salinity on oil recovery from shales to understand potential implications of this shift. Additionally, few studies have been documented on low salinity brines (LSBs) as the fracturing fluids for stimulation applications in liquids-rich shale plays with or without surfactant. This paper discusses a study in which low salinity surfactant solutions were injected into the Muskwa shale rock from Canada. Laboratory results suggest that LSBs ([LSBs], ≤4% KCl) extract more hydrocarbon than high salinity brines ([HSBs], ≥8% KCl). Notably, additional oil recovery was observed when surfactant is used in LSB. To explore the mechanisms, interfacial tension, emulsion tendency, and reservoir on a chip (ROC) were performed. Interfacial tension reduction was not observed for LSB with surfactants. However, short-lived emulsions were observed in LSB in the presence of a surfactant. Additionally, LSB with surfactant were injected into a microfluidic based ROC device, where the pore size was comparable to that of shale rocks, and the oil recovery that was visualized on ROC was consistent with that found in core flooding tests, and shows the benefit of injection of low salinity surfactant solutions. Based on such observations, two-step mechanisms are proposed for improved oil recovery (IOR) for low salinity surfactant injection: (1) destabilized oil layers (oil/rock) and enhanced pair interaction (surfactant/oil) extract more oil globules and (2) a short-lived emulsion formed by surfactants enables a higher tendency to mobilize the oil globules. The results suggest a potential methodology for optimizing source water prior to fracturing operations. This study strongly suggests that low salinity and surfactant additives optimization are imperative to enhanced well productivity from liquids-rich shale plays.
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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".