Model of Prediction of Gas Emission based on Gas Content Uncertainty in Coal Mine using Geostatistical Simulation and Monte Carlo Method

Abstract

It is very difficult to predict the emission of coal gas before the extraction, because it depends on various geological, geographical and operational factors. Gas content is a very important parameter for assessing gas emission in the coal seam during and after the extraction. Large amounts of gas released during the mining cause concern about adequate airflow for the ventilation and worker safety. Hence, the performance of the ventilation system is very important in an underground mine. In this paper, the gas content uncertainty in a coal seam is first investigated using the central data of 64 exploratory boreholes. After identifying the important coal seams in terms of gas emission, the variogram modeling for gas content was performed to define the distribution. Consecutive simulations were run for the random evaluation of gas content. Then, a method was proposed to predict gas emission based on the Monte Carlo random simulation method. In order to improve the reliability and precision of gas emission prediction, various factors affecting the gas emission were investigated and the main factors determining the gas emission were identified based on a sensitivity analysis on the mine data. This method produced relative and average errors of 2% and 0.57%, respectively. The results showed that the proposed model is accurate enough to determine the amount of emitted gas and ventilation. In addition, the predicted value was basically consistent with the actual value and the gas emission prediction method based on the uncertainty theory is reliable.