Lost circulation early warning model based on heterogeneous integration
Drilling lost circulation accidents are characterized by abruptness and difficulty in control.Therefore,it is urgent to establish an effective lost circulation prediction.In this study,Stacking,a heterogeneous integrator combined with stochastic forest support vector machine and back propagation neural network model,was applied to Yingxi area of Qaidam Basin,Qinghai Province.Firstly,the data set of the target block is processed,and ten parameters with high correlation are selected by grey correlation,and then two layers of stacking integration are set up.The first layer selects random for-est,support vector machine and back propagation neural network model as the basic learning device,and the second layer selects logistic regression model as the meta-learning device.The results show that the heterogeneous ensemble model improves the prediction accuracy(0.981 accuracy,0.970 pre-cision,0.963 recall,and 0.960 F1 score)and overcomes the limitations of homogeneous classifiers.The importance of considering various geological factors in comprehensive lost circulation early warn-ing and prediction is emphasized.
lost circulationheterogeneous integrated modelrandom forestintelligent early warning
万方数据
维普
