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Record W4401479249 · doi:10.56952/arma-2024-0633

Numerical Simulation of Gas Leakage During Controlled Retracting Injection Point Process for Underground Coal Gasification

2024· article· en· W4401479249 on OpenAlexaff
Tianduoyi Wang, Juntai Shi, Keliu Wu, Zhangxin Chen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMining and Gasification Technologies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsLeakage (economics)Coal gasificationUnderground coal gasificationComputer simulationMaterials scienceCoal miningCoalPetroleum engineeringEnvironmental scienceProcess (computing)Waste managementProcess engineeringComputer scienceGeologyEngineeringSimulation

Abstract

fetched live from OpenAlex

ABSTRACT: In order to study the specific influence of various factors on gas leakage during the underground coal gasification (UCG), a numerical model of the Controlled Retracting Injection Point (CRIP) process for UCG was established by the computer modeling group software. Considering the geomechanical effect, the gas distribution characteristics during the CRIP process are explored, as well as the effects of gasification pressure difference, production pressure, gasified-gas pressure, reservoir permeability, and water energy on gas migration. Moreover, an assessment system of gas leakage during the UCG process was established based on dominant factors to provide the possibility for quick judgment of gas leakage. The results showed that the relative magnitude of gasifier pressure and initial coalbed pressure determines whether gas leakage occurs. The highest point of gasifier pressure occurs at a distance of 75 meters from the production well in the direction of the gas injection retreat. Gasifier pressure, gasification pressure difference, and permeability were the main factors, which form a complete gas leakage evaluation system. The gas leakage class was divided by 10 degrees, and Class 1 to Class 5 should enhance leak prevention measures. 1. INTRODUCTION The energy sector is a primary field for achieving carbon neutrality and peak carbon emissions. Since the beginning of this century, the world has been gradually transitioning towards a low-carbon energy structure. A new wave of industrial and technological revolutions has sparked a surge in the low-carbon revolution, new energy revolution, and intelligent revolution (Zou et al., 2019). Coal resources play a crucial role in global energy supply, at least for the next quarter-century. However, the environmental issues associated with its combustion partially offset its value (Takyi et al., 2023). Approximately 80% of the coal mined in China is burned directly on the surface for purposes such as power generation and heating (Imran et al., 2014). This utilization of coal resources will bring serious environmental pollution.

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 score0.021
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.025
GPT teacher head0.292
Teacher spread0.267 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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