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Record W4406047224 · doi:10.1021/acsomega.4c08893

Characterization of the Porosity and Permeability of Gasified Coal in UCG Process: An Experimental and Simulation Study

2025· article· en· W4406047224 on OpenAlexaff
Shanshan Chen, Xiaohu Dong, Yanpeng Chen, Zhen Dong, Bin Zhao, Liangliang Jiang, Zhangxin Chen

Bibliographic record

VenueACS Omega · 2025
Typearticle
Languageen
FieldEngineering
TopicMining and Gasification Technologies
Canadian institutionsUniversity of Calgary
FundersPetroChina Company Limited
KeywordsPorosityCoalPermeability (electromagnetism)Underground coal gasificationMaterials scienceComputer simulationEnvironmental scienceCoal miningPetroleum engineeringMineralogyGeologyComposite materialWaste managementEngineeringChemistryMembraneSimulation

Abstract

fetched live from OpenAlex

Under the environment of energy transformation in the world, underground coal gasification (UCG) is an important means to realize the green and clean development and utilization of deep coal resources. Due to a series of complex chemical reactions, the porosity and permeability of coal have changed significantly. Accurately characterizing the porosity and permeability of gasified coal is of great significance to the field screening, production control, and numerical simulation of the UCG project. In this study, the porosity and permeability of coal samples are studied by means of experiment and numerical simulation, respectively. Subsequently, a predictive model for porosity-permeability relationships is established with its feasibility verified by comparison to previous experimental results. In the experimental phase, a nitrogen atmosphere simulates pyrolysis conditions, while an air atmosphere replicates gasification environments. Scanning electron microscopy (SEM) is employed to characterize the porosity and permeability changes in heat-treated coal samples. For the numerical simulation component, through the analysis of UCG physical and chemical processes, the UCG three-dimensional numerical simulation model at the field scale is established by using CMG-STARS simulation software, and the changes in porosity and permeability during coal gasification are analyzed. The result shows that following heat treatment experiments, the average equivalent pore diameter of the coal sample increases to 0.748 μm, an increase of 493.65%, with general expansion observed across pore diameters. Additionally, the average shape factor decreases from 3.20 to 2.584, suggesting enhanced roundness in the porosity characteristics. Post-heat treatment observations reveal an increase in pore quantity alongside expanded diameters, thus indicating significant alterations in pore structure. Through 3D UCG numerical simulation, the evolution of coal porosity and permeability is categorized into three distinct stages: 25-320, 320-750, and 750-1000 °C. This categorization reflects the evolutionary model of the pore structure during coal gasification. In the predictive model, the permeability of coal during pyrolysis and gasification is represented as an exponential function of the porosity. As the porosity increases, the growth rate of permeability initially rises slowly before gradually accelerating. Furthermore, it is observed that in the gasification process, the increase in coal permeability with respect to porosity is less pronounced than that observed during pyrolysis. The findings presented in this paper hold significant implications for field screening, process design, and optimization of UCG applications.

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)0.001
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 score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.016
GPT teacher head0.275
Teacher spread0.259 · 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

Citations7
Published2025
Admission routes1
Has abstractyes

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