The Depths of Hydraulic Fracturing and Accompanying Water Use Across the United States
Bibliographic record
Abstract
Reports highlight the safety of hydraulic fracturing for drinking water if it occurs "many hundreds of meters to kilometers underground". To our knowledge, however, no comprehensive analysis of hydraulic fracturing depths exists. Based on fracturing depths and water use for ∼44,000 wells reported between 2010 and 2013, the average fracturing depth across the United States was 8300 ft (∼2500 m). Many wells (6900; 16%) were fractured less than a mile from the surface, and 2600 wells (6%) were fractured above 3000 ft (900 m), particularly in Texas (850 wells), California (720), Arkansas (310), and Wyoming (300). Average water use per well nationally was 2,400,000 gallons (9,200,000 L), led by Arkansas (5,200,000 gallons), Louisiana (5,100,000 gallons), West Virginia (5,000,000 gallons), and Pennsylvania (4,500,000 gallons). Two thousand wells (∼5%) shallower than one mile and 350 wells (∼1%) shallower than 3000 ft were hydraulically fractured with >1 million gallons of water, particularly in Arkansas, New Mexico, Texas, Pennsylvania, and California. Because hydraulic fractures can propagate 2000 ft upward, shallow wells may warrant special safeguards, including a mandatory registry of locations, full chemical disclosure, and, where horizontal drilling is used, predrilling water testing to a radius 1000 ft beyond the greatest lateral extent.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".