Conventional Oil—The Forgotten Part of the Water‐Energy Nexus
Bibliographic record
Abstract
The impacts of unconventional oil and gas production via high-volume hydraulic fracturing (HVHF) on water resources, such as water use, groundwater and surface water contamination, and disposal of produced waters, have received a great deal of attention over the past decade. Conventional oil and gas production (e.g., enhanced oil recovery [EOR]), which has been occurring for more than a century in some areas of North America, shares the same environmental concerns, but has received comparatively little attention. Here, we compare the amount of produced water versus saltwater disposal (SWD) and injection for EOR in several prolific hydrocarbon producing regions in the United States and Canada. The total volume of saline and fresh to brackish water injected into depleted oil fields and nonproductive formations is greater than the total volume of produced waters in most regions. The addition of fresh to brackish "makeup" water for EOR may account for the net gain of subsurface water. The total amount of water injected and produced for conventional oil and gas production is greater than that associated with HVHF and unconventional oil and gas production by well over a factor of 10. Reservoir pressure increases from EOR and SWD wells are low compared to injection of fluids for HVHF, however, the longer duration of injections could allow for greater solute transport distances and potential for contamination. Attention should be refocused from the subsurface environmental impacts of HVHF to the oil and gas industry as a whole.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".