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Record W6977371764 · doi:10.6084/m9.figshare.28938872

A Shift in Mindset for the Oil and Gas Industry by Ina Tan

2025· article· en· W6977371764 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2025
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsnot available
Fundersnot available
KeywordsMidstreamPetroleum industryMindsetSWOT analysisDownstream (manufacturing)Natural gas industryResource (disambiguation)Upstream (networking)

Abstract

fetched live from OpenAlex

The oil and gas industry has always been a foundational aspect of any economy. For a long time, it has been thought that an economy simply cannot thrive and survive without an oil & gas backbone. Longwell (2002) firmly believes that the industrial revolution has pushed the complete necessity of this industry. Not a single country has thrived without a resource as necessary as the fossil fuel.The study begins by going into detail about the definition of the industry. It looks into a typical full scale oil & gas company with an upstream, midstream and downstream business. It defines these sectors and goes into detail about this industry’s impact to economy and the environment. The author then delves into an analysis of the Canadian market and how significant the industry’s impact is to the economy. This section gives us an idea into the role of the industry in this specific market. A further analysis on the industry is performed using a Porter’s Five Forces analysis and ending it with key takeaways on the industry as a whole.Key players of the industry in the Canadian market are then introduced and further analyzed using a SWOT analysis. Here the sense of similarity and overarching themes across the key players are realized. Interestingly, most literature gathered may actually be true to life in these key players.Prior to concluding, we look at the current general direction of the industry. Here, it was fascinating to see that more of the “old-tech” habits of the industry are emphasized. A lot of valuable takeaways here from a variety of authors concluding similar thoughts, the industry has not been able to see much value in investment in research and innovation. It then focuses on a particular innovation that may eventually be difficult to ignore – renewable energy and the slow but eventual rise of electric vehicles.Drawing on over a decade of industry experience, the author found this study particularly engaging, offering insights that align closely with real-world experiences. The study also proved to be enlightening and pushed the author to reimagine an alternate future. This study is aimed to raise questions and provoke, not only for people within the industry, but the general public.

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 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 categoriesInsufficient payload (model declined to judge)
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 score0.622
Threshold uncertainty score0.999

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.025
GPT teacher head0.287
Teacher spread0.262 · 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 teacher head, not a consensus.

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

Citations0
Published2025
Admission routes1
Has abstractyes

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