Bibliographic record
Abstract
Guest editorial Risks are inherent in every forward-looking business decision. As a result, there has been a great deal of work done and resources invested in risk management in the oil and gas industry in recent years. Financial and regulatory risks have been the focus of much of this effort. But more recently, companies have started including operational risks, prioritizing them and thinking about how they can manage and monitor all risks in a coordinated way. In collaboration with Oxford Analytica, Ernst & Young examined the strategic risks facing oil and gas companies. This study was not a random selection exercise but rather a structured consultation with industry leaders and subject matter professionals from around the world (Fig. 1). What follows are the top 10 identified strategic risks for oil and gas companies. 1. Human Capital Deficit The growing human capital deficit in the sector has become a significant strategic threat to the industry. One study participant set out the issue: "The ability of the oil and gas services sector to expand sufficiently to meet future demand growth is questionable, not least in terms of staff. Project delays and abandonment are as much a result of capacity constraints as financial calculations, although the two are intimately linked." 2. Worsening Fiscal Terms Worsening fiscal terms are seen as a high risk. In some cases, this is due to energy nationalism, although in others it is purely the result of political opportunism and high prices. Tax regime changes can spur additional oil and gas industry restructuring in countries such as Canada, Venezuela, Russia, and Algeria. The impact of political opportunism and high prices is a device that has been seen in both the developing and nondeveloping world. 3. Cost Controls The third operational threat is the inability to control costs. This threat was considered great enough to have a strategic impact, and a failure to manage the threat could undermine the competitiveness of oil and gas companies. Participants agreed that the problem extends from exploration all the way through the value chain, impacting everything from refinery build costs to pipeline construction. The Upstream Capital Costs Index, which measures cost inflation in oil and gas projects, has gone up by 79% since 2000, with most of that increase coming since May 2005.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.040 | 0.011 |
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".