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Record W1996669241 · doi:10.2118/159732-ms

Efficiency of Capital Expenditure of Petroleum Producers: How Investing in Exploration and Development Would Affect Reserves and Production

2012· article· en· W1996669241 on OpenAlexaboutno aff
Lev Virine

Bibliographic record

VenueSPE Hydrocarbon Economics and Evaluation Symposium · 2012
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsnot available
Fundersnot available
KeywordsCapital expenditureProduction (economics)Investment (military)Natural resource economicsBusinessPetroleumAffect (linguistics)Operating expenseCapital investmentCapital (architecture)Fossil fuelEconomicsIndustrial organizationFinanceMicroeconomicsEngineering

Abstract

fetched live from OpenAlex

Abstract Petroleum producers are currently engaged in significant expenditure towards exploration and development in various oil and gas plays in the United States and Canada. Does such expenditure lead to better corporate results, particularly reserves and production, or are there other factors which influence corporate results? The methodology for analysis of efficiency of capital expenditure was developed, based on the correlation between different types of exploration expenditure, including land, drilling and seismic and producer's reserves and production. A model was developed to quantitatively assess exploration and development expenditure, production, and reserves for various producers for different oil and gas plays. The model employs a number of Key Performance Indicators (KPIs) such as finding and development costs with and without acquisitions, reserves life, reinvestment, and others. The methodology was applied to a comprehensive study of finding and development expenditure in Western Canada. The study did not find a correlation between exploration and development expenditure and company production and reserves additions for WCSB as a whole. However, a correlation was found between expenditure of the companies belonging to the particular group and production within a particular strategy. The results imply that company results are most sensitive to the high level business decisions rather than overall investment in exploration and development.

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)0.001
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 score0.054
Threshold uncertainty score0.499

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.042
GPT teacher head0.267
Teacher spread0.225 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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
Published2012
Admission routes1
Has abstractyes

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