Fit-for-Purpose Water Treatment in Permian Shale – Field Data, Lab Data and Comprehensive Overview
Bibliographic record
Abstract
Abstract Sourcing water for hydraulic fracturing and disposing of produced water are well-known constraints and significant cost items in the development of shale formations in the Permian Basin. Utilizing a water life-cycle approach, some of the produced water can be treated and reused. However, there is usually more produced water than needed and some must be disposed, typically by injection into a disposal well. Whether the water is to be reused or disposaed, it must be treated to some extent. Given the volumes of water involved, treatment technology must be robust and inexpensive. This suggests that the selected technology should be tailored to the characteristics of the water and the quality requirements of the final purpose (reuse or disposal). This paper starts with characterization of produced water from a few Permian shale fields and proceeds to the selection of appropriate conventional, robust and low cost treatment systems. Using this approach, fit-for-purpose treatment systems have been implemented in the field. Impairment problems with reuse and disposal of this treated produced water have decreased.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.001 | 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".