Numerical Simulation of Gas Leakage During Controlled Retracting Injection Point Process for Underground Coal Gasification
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Bibliographic record
Abstract
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.
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Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it