Development of Peracetic Acid (PAA) as an Environmentally Safe Biocide for Water Treatment During Hydraulic Fracturing Applications
Why this work is in the frame
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Bibliographic record
Abstract
Abstract In the past years, unconventional oil and natural gas production has steadily increased in the United States. Driven by the development of new technologies such as horizontal drilling and hydraulic fracturing, shale gas has led to major increases in reserves of oil and natural gas. During hydraulic fracturing, water and chemicals are injected, at high pressure, into the formation to increase the fractures in the rock layer and allow hydrocarbons to flow. Because large quantities of water are used during this process, the need for water treatment and reuse has become critical. Water treatment prevents the introduction of microorganisms in the formation, which can potentially result in problems such as reservoir souring, biofouling and microbiologically induced corrosion (MIC). Additionally, cleaning of produced water reduces the constant demand for fresh water. Based upon these concerns, we have developed an improved formulation of the oxidizing biocide peracetic acid (PAA). To determine the potential use of PAA in fracturing application, we conducted a number of tests in laboratory and field conditions. We found that this improved chemistry shows superior results when compared to other conventional biocides (glutaraldehyde, THPS, etc), including faster and persistent microbial kill, lower corrosivity, water cleanup properties and solids dropout. We also found that PAA had no adverse effects on other chemistries present in the frac fluids, such as friction reducers and scale inhibitors. Additionally, PAA is a "green" chemistry as it breaks down into innocuous components. Altogether, PAA represents an extremely efficient and environmentally safe option for treatment of water used for hydraulic fracturing.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| 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 it