Bibliographic record
Abstract
This year’s HSE Technical Focus feature showcases environmental sustainability. Offshore hybrid renewable power generation, electrically driven well abandonment in urban and natural settings, and the use of remotely controlled, low-carbon-footprint underwater intervention/inspection drones demonstrate HSE- and sustainability-conscious planning and successful implementation in the field. Environmental, social, and governance guidelines, as well as regulatory and sustainability-related frameworks, provide guidance to achieve the social license to operate. Risk management and stakeholder engagement practices help predict nontechnical risks and keep the community informed. In paper SPE 204901, which involves using renewable energy sources for offshore oil and gas platform power generation, one operator proposes harnessing wind energy in weak-wind areas by replacing gas-turbine generators with offshore wind turbines adapted to low-wind-speed regions, thereby lowering maintenance costs and carbon exposure. In paper OTC 31394, a service company, acting as project management lead, and an operator collaborate to use fully electrified equipment to abandon wells in sensitive areas such as urban and natural settings where smell and noise are significant issues. In paper OTC 32481, an operator advocates the use of underwater intervention/inspection drones for offshore projects and describes their advantages, including operational risk reduction, lower carbon footprint, lower costs, reduced intervention time, and improved quality and frequency of inspection data. One of the recommended-reading papers, SPE 210788, describes how an operator advocates for mental health or “mental hygiene,” a “preventive measure for sustaining good emotional health” to mitigate workers’ distress in the oil and gas industry and an indication of the growing emphasis upon total wellness. Many more examples of recent notable achievements in HSES can be found in the list of recommended additional reading and in the OnePetro online library. Knowledge sharing, on the local and global levels, encourages collaboration and solutions generation. See the SPE Health, Safety, Environment and Sustainability website for more information and to learn how you could be involved. Recommended additional reading at OnePetro: www.onepetro.org. SPE 210788 Shifting Paradigm of Mental Hygiene—A Novel Approach To Mitigate Workers’ Distress in the Oil and Gas Industry by Adeela Khalid, RPM, et al. SPE 212791 An Improved Work Flow in Mass-Balance Approach for Estimating Regional Methane Emission Rate Using Satellite Measurements by Jeffrey Y. Bian, University of Alberta, et al. SPE 210245 Insights From Benchmarking Methane Emissions of Oil and Natural Gas Production in the United States by Tom Curry, ERM, et al.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.009 | 0.004 |
| Insufficient payload (model declined to judge) | 0.387 | 0.333 |
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".