MétaCan
Menu
Back to cohort
Record W4303710001 · doi:10.46690/ager.2023.01.07

Understanding hydraulic fracture mechanisms: From the laboratory to numerical modelling

2022· article· en· W4303710001 on OpenAlexafffund
Aly Abdelaziz, Johnson Ha, Mei Li, Earl Magsipoc, Lei Sun, Giovanni Grasselli

Bibliographic record

VenueADVANCES IN GEO-ENERGY RESEARCH · 2022
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaEnergi Simulation
KeywordsFracture (geology)GeologyHydraulic fracturingBedGridMultiphysicsAnisotropyOil shaleGeotechnical engineeringBeddingMechanicsGeometryStructural engineeringMathematicsEngineeringFinite element method

Abstract

fetched live from OpenAlex

The development of fracture networks associated with hydraulic fracturing operations are extremely complex multiphysics processes and there is still no accepted methodology for mapping or realistic recreating such fracture networks. This is an issue especially for modeling purposes, as, ideally, an accurate numerical representation, and subsequent numerical model, should be able to honor the trajectory, type, connectivity, and geometric properties of the complex fracture network generated. This research proposes a novel framework capable of conducting fluid flow numerical simulations based on mapped fracture networks induced during hydraulic fracturing laboratory experiments where a shale sample, under true triaxial reservoir stress conditions, is subjected to fluid injection to mimic a single stage open-hole in-situ hydraulic fracture operation. The resulting post-test fracture network of the shale sample is filled with fluorescent dyed epoxy and subsequently imaged. The images are segmented, and individual fractures are classified based on their geometrical characteristics, as parted bedding planes, opened natural fractures, and newly generated hydraulic fractures. The digital fracture network is numerically represented for fluid flow simulation using a dual-porosity model within the finite volume method. In the numerical reconstruction, fractures are implicitly represented in a set of cells with virtual fracture aperture. The properties of each grid cell are assigned based on fracture classification, and flow between grid cells is explicitly assigned based on the connectivity of the grid cells. Findings show faster fluid drainage parallel to bedding planes (horizontal) than in the vertical direction, indicating strong fluid flow anisotropy. Cited as: Abdelaziz, A., Ha, J., Li, M., Magsipoc, E., Sun, L., Grasselli, G. Understanding hydraulic fracture mechanisms: From the laboratory to numerical modelling. Advances in Geo-Energy Research, 2023, 7(1): 66-68. https://doi.org/10.46690/ager.2023.01.07

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)0.002
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: none
Teacher disagreement score0.005
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.002
Scholarly communication0.0020.004
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.051
GPT teacher head0.294
Teacher spread0.243 · 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

Citations44
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueADVANCES IN GEO-ENERGY RESEARCHSame topicHydraulic Fracturing and Reservoir AnalysisFrench-language works237,207