气体钻井随钻安全风险智能识别方法

针对目前智能钻井技术在工况表征、样本收集整理、数据处理及特征提取方面的不足,建立随钻安全风险智能识别方法.使用相关性分析法,确定表征气体钻井安全风险的关联参数;收集并整理20余井次安全风险时段监测数据,建立气体钻井多种安全风险数据样本库,并使用少样本扩展方法扩充样本数量.根据气体钻井随钻监测数据样本形式,设计两层卷积神...

An intelligent identification method of safety risk while drilling in gas drilling

In view of the shortcomings of current intelligent drilling technology in drilling condition representation,sample collection,data processing and feature extraction,an intelligent identification method of safety risk while drilling was established.The correlation analysis method was used to determine correlation parameters indicating gas drilling safety risk.By collecting monitoring data in the safety risk period of more than 20 wells,a sample database of a variety of safety risks in gas drilling was established,and the number of samples was expanded by using the method of few-shot learning.According to the forms of gas drilling monitoring data samples,a two-layer convolution neural network architecture was designed,and multiple convolution cores of different sizes and weights were set to realize the vertical and horizontal convolution computations of samples to extract and learn the variation law and correlation characteristics of multiple monitoring parameters.Finally,based on the training results of neural network,samples of different kinds of safety risks were selected to enhance the recognition accuracy.Compared with the traditional BP(error back propagation)full-connected neural network architecture,this method can more deeply and effectively identify safety risk characteristics in gas drilling,and thus identify and predict risks in advance,which is conducive to avoid and quickly solve safety risks while drilling.Field application has proved that this method has an identification accuracy of various safety risks while drilling in the process of gas drilling of about 90%and is practical.