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

作　　者：HU Wanjun XIA Wenhe LI Yongjie JIANG Jun LI Gao CHEN Yijian 

机构地区：[1]School of Electrical Engineering and Information,Southwest Petroleum University,Chengdu 610500,China [2]State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation,Chengdu 610500,China [3]School of Oil&Gas Engineering,Southwest Petroleum University,Chengdu 610500,China [4]School of Computer Science,Southwest Petroleum University,Chengdu 610500,China

出　　处：《Petroleum Exploration and Development》2022年第2期428-437,共10页石油勘探与开发（英文版）

基　　金：Supported by National Key R&D Plan (2019YFA0708303);Key R&D Projects of Sichuan Science and Technology Plan (2021YFG0318);Key Projects of NSFC (61731016)。

摘　　要：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.

关 键 词：gas drilling safety risk intelligent risk identification few-shot learning convolution neural network measurement while drilling 

分 类 号：TE28[石油与天然气工程—油气井工程]