Taking the hydro out of hydrofracturing: Application of ultra-light weight proppants to cryogenic liquid nitrogen as a fracturing fluid
Bibliographic record
Abstract
In the last decade, hydraulic fracturing has rapidly gained popularity worldwide, emerging as the leading method of natural gas extraction in the United States. However, the practice remains controversial due to its contribution to greenhouse gas emissions and the contamination of freshwater used in fracturing fluids. Although waterless fracturing fluids have been developed, including those using N2, CO2, oil, and alcohol, their application has been limited largely due to reduced fracturing power. Recent research has demonstrated that cryogenic nitrogen may prove a viable alternative, if this issue is properly addressed. Addition of durable, lightweight proppants is one way to increase fracturing power. This study aims to investigate the effect of proppant addition on the fracturing capabilities of cryogenic nitrogen. Three ultra-lightweight proppants will be combined with liquid nitrogen and fracturing power will be measured using triaxial stress tests. This novel approach has not yet been explored and will open more avenues of research into sustainable and efficient fracturing using cryogenic nitrogen.
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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) | 0.001 | 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".