Bibliographic record
Abstract
Editor's column Sustainability and environmental issues played a key part in the content at two of SPE’s recent major conferences. Offshore Europe was held in Aberdeen in September and the Annual Technical Conference and Exhibition (ATCE) took place for the first time in Canada in late September-early•October. Days before the ATCE conference began, activists held rallies around the world, including in Canada, demanding action on climate change, and the issue has risen to the forefront of political campaigns in Europe and the US. Three presidential candidates in the US have promised to “ban” hydraulic fracturing for environmental reasons if elected, although not offering specifics. At the United Nation’s climate change summit in September, more than 100 international banks pledged to decrease investment in the oil and gas sector because of the issue. The bankers promised to support full implementation of the Paris climate agreement and increase investments in renewables in lieu of hydrocarbons. The oil and gas industry is paying attention, as government and financial scrutiny of it increases and as public momentum for environmental regulation grows. But the scrutiny comes at a time when global energy consumption also is increasing, particularly in developing counties. Even with increased support for renewable forms of energy, oil and gas will play a major role in meeting world demand for years to come. At the same time the climate summit was taking place, the Oil and Gas Climate Initiative (OGCI)—a CEO-led group representing international and national oil companies—met in New York and announced further plans to accelerate the reduction of greenhouse gas emissions and support the goals of the Paris agreement. The group outlined a proposal to encourage investment in carbon capture and storage, and reported progress toward its goal of reducing industry methane emissions. OGCI said its member companies met its “methane intensity target” by cutting intensity 9% in 2018. It is now working to reduce carbon intensity in upstream operations by 2025. Members of the OGCI include ExxonMobil, Chevron, Shell, BP, Total, Equinor, Occidental, Eni, Repsol, Saudi Aramco, Petrobras, and CNPC. Both individual companies and other organizations are also taking steps to reduce the industry’s environmental footprint. During the Offshore Europe conference, The Oil & Gas Technology Centre announced it will create with industry a Net Zero Solution Centre to accelerate the development and deployment of technologies to de carbonize offshore operations and develop the UK continental shelf as the first global net zero oil and gas basin. The center is backed by several companies, including BP, Shell, Total, and Equinor. Much of the industry’s response hangs on the viability of carbon capture, utilization, and storage. The International Energy Agency describes it as one of the only technology solutions that can significantly reduce emissions across key industries. An article beginning on page 24 of this issue outlines the industry’s progress in this initiative and the challenges to be met.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".