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Record W2171866900

Natural Gas Production From Tight Gas Formations: A Global Perspective

2008· article· en· W2171866900 on OpenAlexaff
Roberto F. Aguilera, Thomas G. Harding, Federico F. Krause

Bibliographic record

Venue19th World Petroleum Congress · 2008
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsNatural gasTight gasTight oilUnconventional oilFossil fuelGeologyPetroleum engineeringOil shaleShale gasPetroleumHydraulic fracturingPaleontologyEngineeringWaste management
DOInot available

Abstract

fetched live from OpenAlex

Tight gas formations are part of what is usually known as unconventional gas which also includes coal bed methane, shale gas and natural gas hydrates. Tight gas formations as used in this paper refer to sandstone and carbonate reservoirs with in-situ effective permeabilities to gas equal to or smaller than 0.1 md. In petroleum provinces in North America some tight gas reservoirs are found in basincentered or continuous gas accumulations. Others are found in low permeability reservoirs in conventional structural, stratigraphic or combination traps usually referred to as sweet spots. A limited amount of information suggests that tight gas formations are generally found in older rocks in the same petroleum provinces where conventional gas is produced. This observation, supported by various examples and illustrated with a gas resource pyramid, permits using conventional gas formations as a proxy for the presence of tight gas in basins and petroleum provinces throughout the world. A variable shape distribution (VSD) model leads to the conclusion that there is a significant potential endowment in tight gas formations that rivals the endowment from conventional gas accumulations (15,100 tcf). Thus, tight gas formations have potential to provide a significant contribution to global energy demand estimated at approximately 722 quads by 2030. It is recommended to actively pursue research and development of this potential. The economic and technical challenges involved in commercialization of this vast untapped resource are many and overcoming them will depend on a multi-disciplinary approach involving geoscience, engineering and economics. In particular, resource characterization and production technologies will be discussed and areas for future research presented.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0000.001
Scholarly communication0.0020.004
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.011
GPT teacher head0.231
Teacher spread0.220 · 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 designObservational
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

Citations20
Published2008
Admission routes1
Has abstractyes

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