Drilling risk real-time predictive based on support vector machines

For there are higher complexity,uncertainty of underground states and inaccuracy of some measured parameter in the drilling process,the potential risk can not be judged accurately in time,and it even will cause drilling accident.In this paper,a drilling risk predictive method based on support vector machines was presented.Statistical learning theory was a small-sample statistical method,support vector machines was the most effective method in the statistical learning theory which could get accurate mathematical model though small sample,it has very strong practical significance.The support vector machine was used in data fusion,and the drilling risk predication model was trained by the measured data in drilling process.This model could be used to realize state monitoring in the process of drilling.Sixteen samples in the drilling process were used here,eight of it as training sample,and the other as test sample.Simulation results showed the proposed drilling risk predictive method based on support vector machines was effective.