Alternative Management of Oil and Gas Produced Water Requires More Research on Its Hazards and Risks
Bibliographic record
Abstract
Produced water is the largest waste stream associated with oil and gas exploration and production operations. Most produced water generated onshore is managed by permitted injection in deep underground wells, but alternative disposal options including reuse are increasingly being considered. However, insufficient understanding of the composition and toxicity of produced water imposes significant constraints on effective management of potential short-term and long-term risks associated with such alternative uses. As interest builds for management options, such as surface discharge, livestock watering, irrigation, and other industrial uses, research is needed to assess produced-water hazards and exposures to both humans and the environment. This challenge affords an opportunity to capitalize on emerging risk assessment tools. Innovative and comprehensive approaches to filling data gaps and assessing produced water risks will be imperative. A group of experts from industry, academia, and government were assembled to define research needs to support objective decision making on the acceptability, or lack thereof, of produced water disposal alternatives. Presented here are key outcomes from that workshop and recommendations for a research framework to assess toxicity of produced water and associated risks from above ground discharge and reuse options. Integr Environ Assess Manag 2019;15:677-682. © 2019 SETAC.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".