利用人工神经网络法中的自适应共振理论优选钻头 ,将定性、定量优选因素作为输入层神经元 ,形成一种综合性选型方法 .将优选的钻头分别在大庆的林深 2井和肇深 8井进行现场实验 ,结果表明 :2口实验井的三开平均机械钻速比以前提高 2 0 .6% ,三开行程钻速提高 2 1.2 % ,单只钻头进尺提高 98.1% ,提高深井钻井速度的效果明显 .此种优选钻头方法可行 By using the adaptive resonance theory in artificial neural network, the optimal drill bit is selected, and the qualitative and quantitative optimization factors are taken as the input layer neurons to form a comprehensive selection method. 8 wells. The results show that the average penetration rate of three open wells in the two experimental wells is 20.6% higher than that in the previous study, the drilling speed in the three-open range is increased by 1.2 1.2%, the drilling footage of a single drill bit is increased by 98.1% The speed of the effect is obvious.This preferred drill method is feasible