Bibliographic record
Abstract
Energized Fractures In the years since George Mitchell’s engineers first used the cocktail of water, sand, and a small batch of chemicals called slickwater to crack open the Barnett Shale in north Texas, trillions of gallons of the low-viscosity mixture have been pumped into shale formations all over the United States and Canada. While the resulting shale revolution owes much of its success to the use of slickwater, it has come at a high cost in terms of dollars and increased public scrutiny. In response, a growing chorus of suppliers, researchers, and service companies are on a mission to get operators working in North American shale plays to re-examine their almost exclusive use of slickwater and consider displacing large volumes of it with carbon dioxide (CO2) and nitrogen (N2). Those in the industry pushing the change say the “energized” fracturing of horizontal wells is a proven technology that stands to improve the economics of completions and the productivity of horizontal oil and gas wells. They point to a growing body of evidence from both Canada and the US that shows energized fractures greatly reduce the amount of water and proppant required to stimulate shale formations, and have the potential to increase recovery rates substantially. Internationally, the technology could help speed up lagging unconventional shale development by alleviating water scarcity issues.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".