石油勘探与开发 >

2022 , Vol. 49 >Issue 2: 377 - 384

DOI: https://doi.org/10.11698/PED.2022.02.16

石油工程

气体钻井随钻安全风险智能识别方法

  • 胡万俊 ,
  • 夏文鹤 ,
  • 李永杰 ,
  • 蒋俊 ,
  • 李皋 ,
  • 陈一健
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  • 1.西南石油大学电气信息学院,成都 610500;
    2.油气藏地质及开发工程国家重点实验室,成都 610500;
    3.西南石油大学石油与天然气工程学院,成都 610500;
    4.西南石油大学计算机科学学院,成都 610500
胡万俊（1995-）,男,四川泸州人,硕士,西南石油大学在读硕士,主要从事智能算法应用研究。地址：成都市新都区新都大道8号,西南石油大学电气信息学院,邮政编码：610500。E-mail:312919623@qq.com

收稿日期: 2021-06-16

  网络出版日期: 2022-03-16

基金资助

国家重点研发计划（2019YFA0708303）; 四川省科技计划重点研发项目（2021YFG0318）; 国家自然科学基金重点项目（61731016）

收起

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

  • HU Wanjun ,
  • XIA Wenhe ,
  • LI Yongjie ,
  • JIANG Jun ,
  • LI Gao ,
  • CHEN Yijian
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  • 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

Received date: 2021-06-16

  Online published: 2022-03-16

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摘要

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,设计两层卷积神经网络架构,设置多个不同大小及权重的卷积核对样本进行纵横两向卷积运算,提取并学习多个监测参数在钻进过程中的变化规律及关联特征。根据神经网络训练结果,优选各安全风险样本类别以提高识别准确率。与传统的误差反向传播（BP）类全连接神经网络架构相比,设计的方法能更深入有效感知气体钻井安全风险特征,实现高效识别和预测,有利于避免或快速解决随钻安全风险。经现场多次应用证实,气体钻井过程中各类随钻安全风险识别准确率为90%左右,具有良好的实用性。

关键词： 气体钻井; 安全风险; 智能识别; 少样本学习; 卷积神经网络; 随钻监测

本文引用格式

胡万俊 , 夏文鹤 , 李永杰 , 蒋俊 , 李皋 , 陈一健 . 气体钻井随钻安全风险智能识别方法[J]. 石油勘探与开发, 2022 , 49(2) : 377 -384 . DOI: 10.11698/PED.2022.02.16

Abstract

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.

Key words： gas drilling; safety risk; intelligent risk identification; few-shot learning; convolution neural network; measurement while drilling

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