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Record W2583550239 · doi:10.2118/0316-0012-jpt

Comments: Long vs. Short Term

2016· article· en· W2583550239 on OpenAlexaboutno aff
John Donnelly

Bibliographic record

VenueJournal of Petroleum Technology · 2016
Typearticle
Languageen
FieldEnergy
TopicGlobal Energy and Sustainability Research
Canadian institutionsnot available
Fundersnot available
KeywordsEconomicsCoalNatural gasFossil fuelCash flowNatural resource economicsAgricultural economicsQuarter (Canadian coin)Capital expenditureEconomyFinanceEngineeringGeographyWaste management

Abstract

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Editor's column ExxonMobil’s latest long-term energy outlook paints a generally robust picture for oil and natural gas despite the steep fall in hydrocarbon prices and cuts in capital spending. The outlook predicts that the oil and gas share of the energy market will grow and that renewable energy sources will remain only a small share of the total picture. Oil will continue to be the world’s largest energy source, with demand for oil and other liquids growing by 20% from 2014 to 2040, according to ExxonMobil’s The Outlook for Energy: A View to 2040. Coal, which is currently the globe’s second-largest fuel, will decline from providing 25% to 20% of total energy demand as industry uses more fuels with lower CO2 emissions. Natural gas use will increase as it replaces coal as second in consumption. The outlook belies shorter-term predictions for the oil and gas market, which continue to forecast a tough year ahead. IHS CERA believes North American independents will need further capital spending cuts to align spending with cash flow. An analysis of 44 North American E&P companies shows that those firms need to cut spending by another USD 24 billion, or 30%, to maintain a healthy fiscal balance. E&P companies cut their 2016 spending budgets sharply from the previous year, but the price of oil has fallen sharply since the fourth quarter of 2015. Consultancy Wood Mackenzie predicts “another volatile, uncertain, complex, and ambiguous year” with only the most robust or strategically important projects going forward. It projects that exploration spending will be only half of its 2014 peak. The lack of new investment and aging, high-cost fields in some regions will be a challenge for operators, but there are some bright spots for potential investment, especially offshore Mexico and Iran. Wood Mackenzie offered several predictions and milestones to watch for during the rest of the year. “Meaningful” increases in production from Iran are not likely as the country offers new contract terms for upstream projects. Crude exports should increase to about 400,000 B/D as shut-in wells are brought back on stream. Saudi Arabia will maintain current production levels so as not to lose market share to Iran. Declines in spending will hit Africa hard. Output will stagnate in Angola and Nigeria due to its aging fields, high production costs, and lack of investment. North Sea activity also will decline because of lower spending. Rationalization is likely as well as merger and acquisition interest. But production in Russia will maintain current levels of 10.7 million B/D despite the drop in oil prices. In North America, the inventory of drilled but uncompleted wells is at an all-time high. Wood Mackenzie predicts that the draw down on these wells will remain flat compared with 2015 through the first part of this year but will increase significantly in the second half. US Gulf of Mexico deepwater production will reach a new high with an additional 250,000 BOE/D coming on line. This reflects projects that have been in development for years. Mexico’s deepwater bidding round of 10 blocks primarily in the Perdido fold belt will be successful. The acreage prospectivity and favorable contract terms will contribute to its most successful bid round to date. JPT

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.757
Threshold uncertainty score0.362

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.010
GPT teacher head0.277
Teacher spread0.267 · 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 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
Published2016
Admission routes1
Has abstractyes

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