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Record W2045020317 · doi:10.2118/0310-0018-jpt

Technology Partnerships for Challenging Environments

2010· article· en· W2045020317 on OpenAlexaff
Garry P. Mahoney

Bibliographic record

VenueJournal of Petroleum Technology · 2010
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsIntecsea (Canada)
Fundersnot available
KeywordsBusinessEquity (law)Industrial organizationProduction (economics)Economics

Abstract

fetched live from OpenAlex

Guest editorial It is widely accepted today that for the remainder of Hydrocarbon Era, global energy stability will rely increasingly upon crude oil and natural gas recovered from giant offshore fields, many of which will be located far from major markets, in hostile environments such as ultradeepwater and the Arctic. Less understood is the fact that large-scale, multibillion-dollar developments are precisely where the principal technological and economic drivers to push beyond technological boundaries are the strongest and where stakeholder participation and partnering is most complex. The different agendas of the various partners and stakeholders in such developments often compound the challenges of technological complexity. For example, a national oil company typically is seeking to ensure long-term domestic political stability and to invigorate the national economy with development of natural resources. A top-tier international oil company is generally aiming to maintain global production, improve unit production costs, and maintain reserves, while, for most smaller operators, the drivers tend to be rapid and reliable realization of assets that feed dynamic balance sheets. In the case of technology providers—namely vendors, constructors, and fabricators—the drivers tend to be stretching applications of established technology, early capitalization of investment in new equipment/assets, maintaining competitiveness, and vying for market share. Against a backdrop of daunting technological challenges, operators, equity owners, and other stakeholders in demanding frontier developments frequently differ on such fundamental issues as contracting strategies; the timing of project milestones; or the program for technology selection, maturation, and implementation. Opinions are often divided, also, over how to balance the costs and risks of technology selection, or the extent of a given technological stepout. Such disagreements are compounded by the very different business drivers and risk appetites of project partners, stakeholders, and technology providers.

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)0.006
metaresearch head score (Gemma)0.023
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: Empirical · Consensus signal: none
Teacher disagreement score0.047
Threshold uncertainty score0.159

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.023
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0030.003
Scholarly communication0.0130.010
Open science0.0020.007
Research integrity0.0130.017
Insufficient payload (model declined to judge)0.0470.019

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 head0.020
GPT teacher head0.264
Teacher spread0.245 · 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
GenreEmpirical

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
Published2010
Admission routes1
Has abstractyes

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