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Record W2039167611 · doi:10.2118/0711-0037-jpt

Shale Gas: Promising Prospects Worldwide

2011· article· en· W2039167611 on OpenAlexaboutno aff
Robin Beckwith

Bibliographic record

VenueJournal of Petroleum Technology · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsOil shaleGeological surveyChinaShale gasPetroleumMiddle EastGeologyUnconventional oilMining engineeringGeographyArchaeologyPaleontology

Abstract

fetched live from OpenAlex

Shale gas is everywhere. China's estimated technically recoverable shale gas resources, at 1,275 Tcf, are almost 50% greater than those touted in the US. Argentina, with 774 Tcf, contains 150 Tcf more than all of Europe. These numbers were spelled out in “World Shale Gas Resources: An Initial Assessment,” a study released April 2011 under the auspices of the US Energy Information Administration (EIA), which commissioned the study from Advanced Resources International (ARl). China, in fact, ranks first in shale gas resources, followed by the US, Argentina, Europe, Mexico, South Africa, Australia, and Canada. Although the study is preliminary and excludes areas like Russia and the Middle East, there is no doubt shale gas resources exist in abundance worldwide. The numbers ARl arrived at are rough. With more extensive data and more time to assess it, ARl stated, the amounts would be higher. However, Donald L. Gautier, chief of the US Geological Survey (USGS) World Petroleum Project, introduced a note of caution regarding the EIA study's figures. The USGS is in the midst of its own assessment of global continuous accumulations, including technically recoverable gas from source rock systems such as gas shales. Initial results from the first basins assessed will be released within the next few months. According to Gautier, the USGS approach, which is geologically based, probabilistic, and emphasizes application of well performance data from analog shale plays in North America, is quite different from that of ARI. “I wouldn’t be at all surprised if the results are as different as the methodology,” he said. Shale Gas Economic Requisites While shale yields approximately 20% (4.8 Tcf in 2010, according to the EIA) of US natural gas consumption, this resource has yet to contribute more than negligibly in regions elsewhere. Yet many countries, buoyed by and in some cases participating in US shale gas exploitation, appear poised to initiate shale gas development within their borders. However, with a lack of shale drilling and completion services, as well as gas production and transportation infrastructure, promising shale gas reservoirs need at least five to 10 years before production would be economic.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.087
Threshold uncertainty score0.293

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0050.008
Open science0.0010.003
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0870.020

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 head0.008
GPT teacher head0.191
Teacher spread0.183 · 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

Citations22
Published2011
Admission routes1
Has abstractyes

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