Bibliographic record
Abstract
Abstract Water is essential in oil and gas operations. Yet water, particularly fresh water, is a scarce resource in many parts of world now and availability in some regions is predicted to become more constrained in the future. This paper will address industry understanding of water risks and impacts and share examples of water management strategies that are integral to sustainable and efficient operations in the sector. Utilizing several publically available tools for improving understanding and evaluating risk, ConocoPhillips has continued to advance both our internal understanding of our risks and mitigation plans and contributed to raising the industry and stakeholder awareness of water management risks in the sector. Through our work with IPIECA, GEMI (Global Environmental Management Initiative) and other organizations, this paper showcases the evolution of the industry’s understanding, tools and guidance developed to better manage water risks, impacts and adaptation. As co-leader in the development of these tools, and from experience in their internal implementation, ConocoPhillips will share the evolution of the topic and the impact the following tools had in managing risks: The IPIECA Global Water Tool for Oil and Gas, customized in collaboration with the World Business Council for Sustainable Development (WBCSD), gives an overview and visual output of water use and risks for a global portfolio across the oil and gas value chain.The GEMI Local Water Tool for Oil and Gas provides a local-level understanding of water risk and development of asset-specific management plans.Development of a focused internal water strategy and areas of emphasis (supported by the IPIECA and GEMI tools) is expanding our transparency and internal understanding of water risks. The paper will share practical implementation results from utilizing these tools, support implementation of good management practices and environmental and operational performance.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.018 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".