Lab-simulated downhole leaching of formaldehyde from proppants by high performance liquid chromatography (HPLC), headspace gas chromatography-vacuum ultraviolet (HS-GC-VUV) spectroscopy, and headspace gas chromatography-mass spectrometry (HS-GC-MS)
Bibliographic record
Abstract
The ability of different methods to analyze formaldehyde and other leachates from proppants was investigated under lab-simulated downhole conditions. These methods include high performance liquid chromatography (HPLC), headspace gas chromatography-vacuum ultraviolet spectroscopy (HS-GC-VUV), and headspace gas chromatography-mass spectrometry (HS-GC-MS). Two different types of resin-coated proppants, phenol-formaldehyde- and polyurethane-based, were examined. Each proppant was tested at different time intervals (1, 4, 15, 20, or 25 hours) to determine the timeframe for chemical dissolution. Analyses were performed at room temperature and heated (93 °C) to examine how temperature affected the concentration of leachates. Multiple matrices were examined to mimic conditions in subsurface environment including deionized water, a solution surrogate to mimic the ionic concentration of produced water, and recovered produced water. The complexity of these samples was further enhanced to simulate downhole conditions by the addition of shale core. The influence of matrix components on the analysis of formaldehyde was greatly correlated to the quantity of formaldehyde measured. Of the three techniques surveyed, HS-GC-MS was found to be better suited for the analysis of formaldehyde leachates in complex samples. It was found that phenol-formaldehyde resin coated proppants leached higher concentrations of formaldehyde than the polyurethane resin coated proppants.
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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 | 0.000 | 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) | 0.000 | 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".