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Record W2338181459 · doi:10.3997/2214-4609.201600412

Mathematical Simulation of Hydraulic Fracturing of Shale Strata

2016· article· en· W2338181459 on OpenAlexaff
Atena Pirayehgar, Maurice B. Dusseault

Bibliographic record

VenueProceedings · 2016
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsHydraulic fracturingGeologyOil shaleGeothermal gradientPermeability (electromagnetism)Petroleum engineeringSedimentary rockIgneous rockPetrologyFossil fuelGeothermal energyFracture (geology)PetroleumNatural gasSource rockGeotechnical engineeringGeochemistryGeomorphologyGeophysicsStructural basin

Abstract

fetched live from OpenAlex

Summary Because of continuing demand for energy from fossil fuels, low-permeability hydrocarbon-bearing rocks such as stiff shales and tight sandstones are regularly stimulated using hydraulic fracturing technology ( Curtis 2002 ). In a similar vein, development of intermediate-grade geothermal resources in low-permeability igneous and sedimentary rocks also requires hydraulic fracture stimulation. Most stiff rocks are naturally fractured, which affects hydraulically induced fracture direction locally, but the global orientation of induced fracture growth remains normal to the minimum principal stress. The key factor for economic success is to maximize the stimulated volume and the surface area across which heat, oil or gas can diffuse.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.028
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.229
Teacher spread0.218 · 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 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
Published2016
Admission routes1
Has abstractyes

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