Produced Water And Flowback From Hydraulic Fracturing Operations And Options For Their Reuse
Bibliographic record
Abstract
As Alberta continues to expand, there is greater strain placed on our water resources. If the province is to meet its growing water demands and maintain a sustainable and competitive economy the government will need to examine alternative water sources. Water reuse presents a potential solution to our upcoming water scarcity. Produced waters/flowback from hydraulic fracturing operations are a potential source of reusable water. Alberta must update their water management and reuse policies, legislations technologies and practices in order to meet our water and energy needs currently and in the future. This paper investigates and reviews the reuse of produced water and flowback from hydraulic fracturing operations for shale gas in Alberta and the regulatory challenges and opportunities associated with this. Specifically, it delves into the quality and quantity of produced water/flowback, potential reuse applications, and environmental considerations. The paper also reviews and compares government regulations in Canada, the United States of America, Alberta, British Columbia and California. Finally, based on the research conducted it outlines potential opportunities for reuse and provides recommendations to promote the reuse of produced water/flowback in Alberta.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".