MétaCan
Menu
Back to cohort
Record W2325259697 · doi:10.2118/0708-0018-jpt

Top 10 Risks for the Oil and Gas Industry

2008· article· en· W2325259697 on OpenAlex
Rob Jessen

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Petroleum Technology · 2008
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsnot available
Fundersnot available
KeywordsPetroleum industryRestructuringPolitical riskBusinessWork (physics)Risk managementFossil fuelEconomicsFinancePoliticsIndustrial organizationEngineering

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.430
Threshold uncertainty score0.260

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.032
GPT teacher head0.291
Teacher spread0.259 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it