Bibliographic record
Abstract
Abstract Surfactants in hydraulic fracturing face challenges like stability under elevated temperature, high salinity, and pH, shear resistance, and compatibility. They complicate waste treatment due to their persistence, potential environmental harm, and impact on water surface tension. While some surfactants degrade into harmless substances, they often slow down when adhering to soil or sand, potentially releasing heavy metals. Inexpensive fatty acid-based surfactants can mitigate these environmental and many operational issues. In this work, fatty acid based viscoelastic biosurfactant (BioSurfUA) is synthesized and tested for their properties for the use in unconventional reservoirs. A series of tests including thermal stability, interfacial tension, rheology, and surface tension were performed under reservoir brine and temperature conditions and compared against industry standards anionic and non-ionic surfactants, and surfactant-nanoparticle formulations. Stability in the presence of iron (iron chloride) was also performed. Moreover, BioSurfUA was also tested for oil recovery performance from tight cores and analyzed for wettability alteration agent, compared with various other surfactant-nanoparticle formulations. The produced BioSurfUA has unique advantages compared to the conventional surfactants as it demonstrated higher viscoelasticity along with ultra-low IFT at extremely low concentrations. At room temperature the shear viscosities of 0.1 wt% BioSurfUA in Tap water were measured as 850 cP, and 70 cP at the shear rate of 0.1s-1, and 100 s-1 respectively. At the higher shear rates, the domination of temperature, and salt seems negligible. In addition, it also showed great stability against a considerable amount of iron (Fe+3), and a range of alkaline pH even at the elevated temperature combinations. The diluted BioSurfUA solutions were able to stabilize the iron sulfide (FeS) in the dispersion form. BioSurfUA showed ultralow IFT (<0.01) oil and outperformed the recovery performance of conventional anionic and nonionic surfactants, and surfactant-nanoparticle formulation at low dosages, thereby offering significant cost savings. BioSurfUA, derived from sustainable and renewable sources, is more environmentally friendly and biodegradable compared to many synthetic surfactants. It addresses existing barriers to using synthetic surfactants, is relatively inexpensive to produce, and has a long shelf life. The BioSurfUA showed excellent interfacial properties, and brine and iron-tolerant behavior at low dosage.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".