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Record W3215641820 · doi:10.2118/1021-0017-jpt

Alaska’s North Slope May Yet See Its Renaissance in Arctic Exploration

2021· article· en· W3215641820 on OpenAlexaboutno aff
Pat Davis Szymczak

Bibliographic record

VenueJournal of Petroleum Technology · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicArctic and Russian Policy Studies
Canadian institutionsnot available
Fundersnot available
KeywordsArcticGeopoliticsFrontierFossil fuelGreenhouse gasPetroleum industryPetroleumThe arcticPoliticsNatural resource economicsEconomyEnvironmental protectionBusinessGeographyOceanographyPolitical scienceEnvironmental scienceGeologyEngineeringEconomicsArchaeologyLaw

Abstract

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It wasn’t too long ago that Arctic oil and gas exploration enjoyed celebrity status as the industry’s last frontier, chock full of gigantic unexplored hydrocarbon deposits just waiting to be developed. Fast forward and less than a decade later, the same climate change that made Arctic oil and gas more accessible has caused an about-face as governments and the world’s supranational energy companies rebrand and target control of greenhouse gases (GHG) to achieve carbon neutrality by 2050. Among countries with Arctic coastlines, Canada has focused its hydrocarbon production on its oil sands which sit well below the Arctic Circle; Greenland has decided to not issue any new offshore exploration licenses (https://jpt.spe.org/greenland-says-no-to-oil-but-yes-to-mining-metals-for-evs), and while Norway is offering licenses in its “High North,” the country can’t find many takers. The Norwegian Petroleum Directorate (NPD) reported that while 26 companies applied for licenses in 2013, this year’s bid round attracted only seven participants. Norway is Europe’s largest oil producer after Russia with half of its recoverable resources still undeveloped and most of that found in the Barents Sea where the NPD says only one oil field and one gas field are producing. That leaves Russia and the US—geopolitical rivals which are each blessed with large Arctic reserves and the infrastructure to develop those riches—but whose oil and gas industries play different roles in each nation’s economy and domestic political intrigues. Russia sees its Arctic reserves, particularly gas reserves, as vital to its national security, considering that oil and gas accounts for 60% of Russian exports and from 15 to 20% of the country’s gross domestic product (GDP), according to Russia’s Skolkovo Energy Centre. With navigation now possible year­round along the Northern Sea Route, Russia’s LNG champion and its largest independent gas producer, Novatek, is moving forward with exploration to expand its resource base and build infrastructure to ship product east to Asia and west to Europe. https://jpt.spe.org/russian­lng­aims­high­leveraging­big­reserves­and­logistical­advantages As a result, Russia’s state­owned majors—Rosneft, Gazprom, and Gazprom Neft—are lining up behind their IOC colleague as new investment in Arctic exploration and development is encouraged and rewarded by the Kremlin. In contrast, the American Petroleum Institute reports that the US oil and gas industry contributes 8% to US GDP, a statistic that enables the US to have a more diverse discussion than Russia about the role that oil and gas may play in any future energy mix. That is unless you happen to be from the state of Alaska where US Arctic oil and gas is synonymous with Alaskan oil and gas, and where the US Geological Survey estimates 27% of global unex­plored oil reserves may lie. Though Alaska is responsible for only 4% of US oil and gas production, those revenues covered two-thirds of Alaska’s state budget in 2020 despite the state’s decline in crude production in 28 of the past 32 years since it peaked at 2 million B/D in 1988, according to the US Energy Information Administration (EIA).

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.102
Threshold uncertainty score0.203

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.002
Scholarly communication0.0060.003
Open science0.0010.004
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0380.012

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 head0.310
Teacher spread0.278 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations1
Published2021
Admission routes1
Has abstractyes

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