Frac Fluid Recycling and Water Conservation: A Case History
Bibliographic record
Abstract
Abstract Shallow gas fracturing is very prevalent in Western Canada. Several thousand wells are typically drilled and completed in the shallow gas fields every year. All these wells are typically hydraulically fractured. Prior to 1999, after testing for micro-toxicity, the flowback fluid was allowed to be land farmed in Southeastern Alberta. In that year, the Alberta Energy and Utilities Board began more stringent enforcement of Guide 58, which required that flowback fluid be disposed in a disposal well. More recently, several years of drought conditions in the shallow gas areas of Southeastern Alberta have caused water shortages. Prior to 1999, the flowback from a particular surfactant-based fracturing fluid could be successfully land farmed. One operator typically had a project of 300 to 400 wells with an average of 5 fracs per day during spring/summer. When the fluid could no longer be land farmed, attempts were made to recycle the flowback fluid. The chemistry of the surfactant gel fluid was insensitive to the water quality, which made the recycling concept successful. Several cost advantages were achieved, which will be detailed in the paper. These included fresh water costs, disposal costs and chemical costs. An additional advantage that was realized involved a 50% reduction in the fresh water requirements for a project. The paper will detail the chemistry of the fracturing gel, its field application, the optimized recycling operation and the details on cost advantages achieved as well as future direction for further reduction in fresh water usage on a project basis.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| 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".