A Safety Assessment Method Based on Online Sequential Extreme Learning Machine (OS-ELM) in Deep Drilling Process
Yupeng Li, Weihua Cao, Chao Gan · 2018
Accurate and timely assessment of drilling system is key for achieving safety and efficiency in deep drilling. In this paper, an online assessment model is proposed by applying online sequential extreme learning machine (OS-ELM). The model has been tested through the actual drilling data for drilling system safety assessment and accidents early warning. By analyzing the mechanism characteristics of accidents, well logging parameters are chosen as the input and accident types are chosen as the output. Owing to the OS-ELM is capable of updating network parameters based on new arriving data without retraining historical data, the model can be updated online for specific formation accidents information to make it more adaptable to a particular environment. The numerical test results show that, comparing with other widely used assessment techniques like support vector machines (SVM) and back propagation (BP), the proposed model has a higher accuracy and shorter recognition time.