CAJ | 학술논문

对录井资料及测井资料与地层可钻性级值的关系进行分析,提出一种基于相关向量机算法的地层可钻性级值预测的新方法.通过标准化钻速、测井声波时差、地层密度、泥质含量和地层深度进行学习训练相关向量机,建立地层可钻性级值预测的相关向量机模型.对准噶尔盆地部分井的地层可钻性级值进行预测的结果表明,该方法优于BP神经网络方法,具有预测精度高、收敛速度快、推广能力强等优点.
대록정자료급측정자료여지층가찬성급치적관계진행분석,제출일충기우상관향량궤산법적지층가찬성급치예측적신방법.통과표준화찬속、측정성파시차、지층밀도、니질함량화지층심도진행학습훈련상관향량궤,건입지층가찬성급치예측적상관향량궤모형.대준갈이분지부분정적지층가찬성급치진행예측적결과표명,해방법우우BP신경망락방법,구유예측정도고、수렴속도쾌、추엄능력강등우점.
The relation between log data, well-log data and formation drillability was analyzed, and a novel method for predicting formation drillability based on relevance vector machine (RVM) was proposed. A prediction model for formation drillability was established using the data of normalized drilling rate, well-log acoustic velocity, formation density, shaliness and formation depth by training the RVM. The proposed method was applied to predict the formation drillability of some wells in Junggar Basin. The results show that the RVM method has higher prediction precision, faster convergence speed and better generalization effect than BP neural network approach.