Leveraging artificial intelligence for water optimisation in upstream oil and gas energy operations
Bibliographic record
Abstract
Abstract Water scarcity and climate change are significant challenges for sustainable water management worldwide. Factors such as population growth, industrial development, and unsustainable practices are increasing water demand. The upstream oil and gas energy industry faces water management challenges, including sourcing, treating, transporting, and disposing of water while meeting Environmental, Social, and Governance (ESG) requirements. This study introduces the Water Usage Efficiency Index (WUEI) using artificial intelligence in Python, a novel quantitative framework aligned with UN Sustainable Development Goals. The WUEI assesses water management in upstream energy operations by analysing water intensity, source sustainability, and temporal variability. Data from the Alberta Energy Regulator and oil sands operators are used to evaluate operational efficiency and water recycling rates from 2013 to 2022. WUEI scores range from 0.624 to 2.130, highlighting areas for improvement and guiding water management strategies. This standardised approach supports ESG objectives and promotes industry best practices. The research offers a practical, AI-enhanced method for evaluating water efficiency in the oil and gas sector, contributing to sustainable water management and ESG goals. Collaboration among academia, industry, and policymakers is essential for the widespread adoption of the WUEI framework.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".