Intelligent risk identification of gas drilling based on nonlinear classification network

Abstract

During the transient process of gas drilling conditions, the monitoring data often has obvious nonlinear fluctuation features, which leads to large classification errors and time delays in the commonly used intelligent classification models. Combined with the structural features of data samples obtained from monitoring while drilling, this paper uses convolution algorithm to extract the correlation features of multiple monitoring while drilling parameters changing with time, and applies RBF network with nonlinear classification ability to classify the features. In the training process, the loss function component based on distance mean square error is used to effectively adjust the best clustering center in RBF. Many field applications show that, the recognition accuracy of the above nonlinear classification network model for gas production, water production and drill sticking is 97.32%, 95.25% and 93.78%. Compared with the traditional convolutional neural network (CNN) model, the network structure not only improves the classification accuracy of conditions in the transition stage of conditions, but also greatly advances the time points of risk identification, especially for the three common risk identification points of gas production, water production and drill sticking, which are advanced by 56, 16 and 8 s. It has won valuable time for the site to take correct risk disposal measures in time, and fully demonstrated the applicability of nonlinear classification neural network in oil and gas field exploration and development.