检索结果-南通市图书馆

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

Petroleum Exploration and Development 2022年第2期49卷 428-437页

作者： HU Wanjun XIA Wenhe LI Yongjie JIANG Jun LI Gao CHEN Yijian School of Electrical Engineering and Information Southwest Petroleum UniversityChengdu 610500China State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation Chengdu 610500China School of Oil&Gas Engineering Southwest Petroleum UniversityChengdu 610500China School of Computer Science Southwest Petroleum UniversityChengdu 610500China

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

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：