Intelligent prediction method for three-dimensional spatial parameters of high-level directional long boreholes

The importance of using the “hole instead of lane” technology to control the gas in the upper corner of the mining area in high-gas mines is becoming more and more important, and this technology can effectively improve the efficiency and safety of gas extraction by precisely controlling the three-dimensional spatial parameters of the drilling holes. Traditional drill hole design relies on theoretical calculations, simulation analysis and empirical judgement, which cannot fully take into account the complexity and variability of the actual working conditions in the mine, and may have certain limitations in actual operation. With the deep development of mining technology towards intelligence and digitalization, the development of a new type of intelligent prediction model through the integration of modern information technology and traditional mining engineering technology has become the key to improve the accuracy of drill hole design. In this study, a BA-BP neural network prediction model based on improved grey-scale correlation analysis is proposed, aiming to optimize the design of three-dimensional spatial parameters for high-level directional drill holes. The model considers several key influencing factors, including mining height, advancement speed of the mining face, uniaxial compressive strength of the overlying rock layer, hard rock lithology scale factor, inclination angle, slant length of the extraction zone and burial depth, etc. Through the comprehensive analysis of these parameters, the model is able to more accurately predict the optimal drilling parameters. In the process of model establishment, the correlation between each influencing factor and drilling effect is first quantitatively evaluated using grey-scale correlation analysis, the main influencing factors are screened out, and the bat algorithm （BA） is used to optimize the weights and bias parameters of the BP neural network, in order to improve the accuracy of prediction and the iteration rate. The effectiveness and reliability of the model are verified by comparing and analyzing the results of the training sample set and the prediction sample set. The experimental results show that the model has high prediction accuracy, with the average error controlled within 4%, fast iteration rate, and high agreement with the actual drilling data. The application of the model can not only realize the intelligence and precision of gas drilling design, but also significantly improve the level of coal mine safety production and gas extraction efficiency, which has a good prospect of popularization and application.