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Record W2772521568

What the future holds for US proppants

2015· article· en· W2772521568 on OpenAlexaboutno aff
Industrial Minerals

Bibliographic record

VenueIndustrial Minerals · 2015
Typearticle
Languageen
FieldEnergy
TopicGlobal Energy and Sustainability Research
Canadian institutionsnot available
Fundersnot available
KeywordsOil shaleGeologyShale gasRange (aeronautics)Mining engineeringOil sandsNatural gasPetroleum engineeringMineralogyArchaeologyGeochemistryGeotechnical engineeringHydrology (agriculture)GeographyEngineeringPaleontologyWaste management
DOInot available

Abstract

fetched live from OpenAlex

Frac sand is typically divided into two types: white sand and brown sand. White sand, the stronger of the two, is typically sourced from the St Peter's Sandstone in Ottawa, Illinois, while brown sand is sourced from the Hickory Sandstone near Brady, Texas. Frac sand is typically used in environments below 6,000 psi. The most notable driver impacting demand is increased proppant loadings, specifically, larger volumes of proppant placed per frac stage. The trend of using larger volumes of finer mesh materials, such as 100 mesh sand and 40/70 sand and ceramics, either on their own or in conjunction with coarser and more conductive proppant tail-ins, continues. Table 1, which is taken from the CSUG report, summarises the estimate of Canada's potentially marketable natural gas resources by type, province and geologic formation. A more recent study by the EIA in 2013 examines areas not addressed in the CSUG report and places Canada's technically recoverable shale gas resources at 573 tcf, or 1.7 times CSUG's upper range estimate for shale gas. The EIA also estimates that Canada has 8.8bn bbls of technically recoverable shale oil.

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)0.001
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.250
Threshold uncertainty score0.426

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.113
GPT teacher head0.333
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 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
Published2015
Admission routes1
Has abstractyes

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