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Record W1987091961 · doi:10.2523/iptc-16945-ms

Environmental Liabilities in Oil and Gas Industry and Life-Cycle Management

2013· article· en· W1987091961 on OpenAlex

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

VenueInternational Petroleum Technology Conference · 2013
Typearticle
Languageen
FieldEngineering
TopicMarine and Offshore Engineering Studies
Canadian institutionsnot available
Fundersnot available
KeywordsNuclear decommissioningLiabilityBusinessAsset managementAsset (computer security)Petroleum industryIT asset managementEnvironmental management systemRisk managementFinanceNatural resource economicsEnvironmental economicsEnvironmental scienceEconomicsEngineeringWaste managementEnvironmental engineering

Abstract

fetched live from OpenAlex

Abstract This extended abstract provides an overview of the environmental liabilities of oil and gas assets, asset retirement obligations (AROs) and environmental reporting requirements under the regulatory regimes in Alberta, in Canada and International Financial Reporting Standards (IFRS). Lifecycle asset liability and environmental management processes are associated with abandonment, remediation and reclamation, decommissioning and closure. Alberta's production of conventional oil and gas and oil sands projects has resulted in increased concerns related to climate changes and environmental liabilities of oilfield assets. More stringent regulatory compliance and standards have been developed and are likely to continue. Operators are responsible for the costs to comply with environmental regulations and take sufficient social responsibilities. The industrial experience of discharging liabilities has indicated that planning at early stages of the operation and managing asset liability bring a reduced cost and risk. Full lifecycle cost-effective and efficient environmental management begins with planning and successful acquisition. The best estimate calculation can be based on internal or external costs, depending on which is most likely.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.569
Threshold uncertainty score0.461

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)0.0000.000

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 head0.004
GPT teacher head0.177
Teacher spread0.173 · 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