Efficacy of Nanofiltration and Reverse Osmosis for the Treatment of Oil-Field Produced Water Intended for Beneficial Reuse
Bibliographic record
Abstract
Treatment and reuse of unconventional oil and gas (UOG) produced water are important strategies that address the dual challenges of water scarcity and pollution posed by UOG production. Considering the high salinity and complex chemistry of UOG produced water, it is important to comprehensively analyze the water quality and potential ecological risk of treated produced water for reuse applications. In this study, we evaluated and compared the efficacy of pretreatment followed by nanofiltration (NF) and reverse osmosis (RO) using membranes of varied permselectivity in treating produced water from the Niobrara Shale play in Colorado. We determined the efficacy of each technology in removing inorganic and organic constituents as well as reducing toxicity on Daphnia magna . Our results show that the pretreatment step resulted in a minor reduction of chemical constituents and toxicity and that the NF permeates did not meet the water quality criteria for irrigation and livestock drinking water. Despite high removal rates for most contaminants in the produced water by RO, the concentrations of chloride and boron as well as the sodium adsorption rate (SAR) in the RO permeates exceeded irrigation guidelines. We observed the passage of surfactants with molecular weights much higher than the molecular weight cutoff of NF and RO membranes, suggesting that membranes are not an absolute barrier to organic contaminants. Our results demonstrate that thorough chemical and toxicological analyses are needed to understand the feasibility and potential risk of treating UOG produced water for beneficial reuse.
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 | 0.001 |
| 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".