MétaCan
Menu
Back to cohort
Record W3045286326 · doi:10.1190/int-2019-0103.1

Replacing conventional brittleness indices determination with new attributes using true hydrofracturing mechanism

2020· article· en· W3045286326 on OpenAlexaff
Ritesh Kumar Sharma, Satinder Chopra, Larry Lines

Bibliographic record

VenueInterpretation · 2020
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsUniversity of CalgaryTrusted Positioning (Canada)
Fundersnot available
KeywordsHydraulic fracturingBrittlenessGeologyShale gasOil shaleModulusPoisson distributionCompactionFracture (geology)Petroleum engineeringGeotechnical engineeringStatisticsMathematicsMaterials science

Abstract

fetched live from OpenAlex

Shale resource plays are associated with low permeability; hence, hydraulic fracturing is required for their stimulation and production. Even though considerable nonuniqueness exists in identifying favorable zones for hydraulic fracturing, geophysicists seem to be avid followers of low-Poisson’s ratio and high-Young’s modulus brittleness criteria, proposed a decade ago. We highlight the misinterpretation that one may run into in following such a criterion for any shale play and develop a new attribute that makes use of strain energy density and fracture toughness. Although the former controls fracture initiation, the propagation of fractures is governed by the latter. Because hydraulic fracturing comprises both these properties, it is firmly believed that the new proposed attribute could be used to highlight the favorable intervals for fracturing. Core data, well log curves, along with mud logs have been used to authenticate the proposed attribute. Finally, computation of the new attributes is implemented on the seismic data with encouraging results.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.543
Threshold uncertainty score0.557

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.015
GPT teacher head0.230
Teacher spread0.216 · 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 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

Citations1
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueInterpretationSame topicHydraulic Fracturing and Reservoir AnalysisFrench-language works237,207