A Water Stewardship Evaluation Model for Oil and Gas Operators
Bibliographic record
Abstract
The rise of the unconventional oil and gas (UOG) industry over the last two decades has transformed the domestic energy outlook but raised concerns over environmental impacts. With the evolution of Environmental Social Governance (ESG) reporting allowing for a transparent view of oilfield operations, the evaluation of corporate sustainability has become increasingly feasible. Even with increased reporting, there have been very few quantifiable metrics for sustainable water management practices in the UOG industry due to the focus being primarily on methane emissions in recent years. This study aims to provide a practical, quantitative, and concise method (two-parameter based - Quadrant Plot) to evaluate UOG operators' performance in minimizing the negative impacts of freshwater use for drilling and fracking. Parameters (%Freshwater and %Salt Water Disposal) used in this performance matrix have been optimized to gather as much information as possible and while being compatible with operators' existing data collection. This study discusses how the Quadrant Plot could quantify the water performance using private and public data from over 20 unconventional oil and gas operators. This quantitative assessment not only enables the determination of a static performance score but also allows for the depiction of changes in performance over time.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".