Research on deep gas geological laws and gas prediction: a case study of the Lvjiatuo coal mine

With the increasing demand for gas, the problem of local anomalies in deep gas urgently needs to be addressed. This study takes the Lvjiatuo coal mine as the research area, analyzes the main influencing factors of coal seam gas accumulation law in the research area, and predicts the deep gas content in the research area based on a neural network model optimized by particle swarm optimization (PSO-BP). The research results indicate that the development of folds and fault structures only plays a certain degree of control over the accumulation and distribution of gas content in the coal seams of Lvjiatuo coal mine, and the burial depth of the coal seams is the dominant factor affecting the deep gas accumulation in the research area; The proportion of mudstone of coal seam roof has a significant impact on gas accumulation, and under similar geological factors, the higher the proportion of mudstone of coal seam roof, the more gas content there is. The PSO-BP neural network model based on the data of coal seam burial depth, structural complexity index, roof proportion of mudstone, and coal seam thickness in the research area can accurately predict the deep gas content in the area, and the predicted values are accurately fitted with the measured values. The research results have important theoretical and practical significance for studying the law of deep gas accumulation and predicting deep gas content.