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Enhanced Coalbed Methane Recovery from Lignite Using CO<sub>2</sub> and N<sub>2</sub>

2025· article· en· W4410600409 on OpenAlexaff
Shaicheng Shen, Zhiming Fang, Xiaochun Li, Quan Jiang, Haimeng Shen, Lu Shi

Bibliographic record

VenueEnergy & Fuels · 2025
Typearticle
Languageen
FieldEngineering
TopicCoal Properties and Utilization
Canadian institutionsGeomechanica (Canada)
FundersNational Key Research and Development Program of China
KeywordsCoalbed methaneMethaneEnvironmental scienceCoalMineralogyChemistryGeochemistryMaterials scienceGeologyCoal mining

Abstract

fetched live from OpenAlex

Experimental studies on enhanced coalbed methane recovery (ECBM) are crucial for advancing our understanding of gas extraction mechanisms. This investigation evaluates the feasibility of CO 2 /N 2 -ECBM application in lignite, a coal type that has received limited attention in prior ECBM studies. The adsorption characteristics of carbon dioxide, methane, and nitrogen in lignite were systematically evaluated, followed by comprehensive CO 2 /N 2 -ECBM experiments employing a self-developed experimental apparatus under various injection strategies. Key findings include the following: (1) At equivalent pressures (0.5–2.5 MPa), the adsorption capacity of carbon dioxide in lignite exceeds that of methane by a factor of 6–7, whereas nitrogen adsorption reaches 78–88% of methane levels. (2) Carbon dioxide injection predominantly occurs through matrix adsorption, achieving 70% storage efficiency with 20% displacement effectiveness. Conversely, nitrogen migration primarily follows fracture networks, yielding 20–24% storage efficiency while enhancing displacement performance. (3) Constant-pressure injection exhibits the lowest methane recovery among tested methods, particularly due to the nondrainable nature of residual methane trapped in semiclosed fracture systems.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.070
Threshold uncertainty score1.000

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 head0.011
GPT teacher head0.213
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.

Study designBench or experimental
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

Citations6
Published2025
Admission routes1
Has abstractyes

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