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Record W4412391302 · doi:10.1063/5.0273550

Gas drainage optimization via fracture network characterization in coal mining

2025· article· en· W4412391302 on OpenAlexaff
Yulin Hu, Quanle Zou, Xiaoyan Sun, Bichuan Zhang, Qican Ran, Fanjie Kong, Q H Li

Bibliographic record

VenuePhysics of Fluids · 2025
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsUniversity of British Columbia
FundersNational Natural Science Foundation of China
KeywordsPhysicsCharacterization (materials science)Petroleum engineeringDrainageFracture (geology)CoalMining engineeringGeotechnical engineeringWaste management

Abstract

fetched live from OpenAlex

The collapse of overlying strata in the goaf creates fractures that facilitate gas migration and accumulation in the upper corner, increasing the risk of gas overlimit. This study used physical simulations to analyze the distribution of the “three vertical zones” in the 11308 working face. The caved zone height is 28.5 m, and the fractured zone height is 54.4 m. The “voussoir beam” structure of mining-induced fractures was identified as the primary gas migration channel. An innovative fracture classification method was proposed to clarify their development and distribution, revealing that the low-level “rectangular terrace” acts as a gas accumulation “sweet spot,” guiding borehole extraction. Numerical simulations indicated that gas mainly accumulates in the high-level of the fracture zone (7–10 times the mining height). Based on these findings, a “low-level interception-high-level extraction” directional long borehole layout was designed, along with a dynamic balance model of “airflow-borehole extraction-gas migration.” Field application in the 11308 working face showed a 123% increase in gas extraction and a stable gas concentration of 0.08% in the upper corner, significantly reducing the risk of upper corner gas exceeding the limit. These results provide valuable guidance for managing gas risks in similar geological conditions in coal mines.

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 score0.914
Threshold uncertainty score0.472

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 head0.004
GPT teacher head0.207
Teacher spread0.202 · 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

Citations10
Published2025
Admission routes1
Has abstractyes

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