Intelligent Kick Detection Method Using Cascaded GRU Network with Adaptive Monitoring Parameters

Drilling sensor failure leads to unavailability of kick monitoring parameters and the inability to apply intelligent kick detection methods. To solve this problem, a confidence evaluation indicator based on softmax is designed to measure the difficulty of kick identification, and the appropriate monitoring parameters are adaptively selected based on this indicator. Finally, an intelligent kick detection method using a cascaded GRU network with adaptive monitoring parameters is proposed in this paper. Kick identification experiments were conducted using simulated and measured data. The experimental results show that, when one monitoring parameter is unavailable, the recognition accuracy of the cascaded network proposed is improved by 10.61% on average and the computational load is reduced by 38.5% compared with the traditional gate recurrent unit network. The applicability of intelligent kick detection methods is significantly improved.